Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM Outputs

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Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM Outputs

Creating efficient prompts for large language models often starts as a simple task… but it doesn’t always stay that way. Initially, following basic best practices seems sufficient: adopt the persona of a specialist, write clear instructions, require a specific response format, and include a few relevant examples. But as requirements multiply, contradictions emerge, and even minor modifications can introduce unexpected failures. What was working perfectly in one prompt version suddenly breaks in another. If you have ever felt trapped in an endless loop of trial and error, adjusting one rule only to see another one fail, you’re not alone! The reality is that traditional prompt optimisation is clearly missing a structured, more scientific approach that will help to ensure reliability. That’s where functional testing for prompt engineering comes in! This approach, inspired by methodologies of experimental science, leverages automated input-output testing with multiple iterations and algorithmic scoring to turn prompt engineering into a measurable, data-driven process. No more guesswork. No more tedious manual validation. Just precise and repeatable results that allow you to fine-tune prompts efficiently and confidently. In this article, we will explore a systematic approach for mastering prompt engineering, which ensures your Llm outputs will be efficient and reliable even for the most complex AI tasks. Balancing precision and consistency in prompt optimisation Adding a large set of rules to a prompt can introduce partial contradictions between rules and lead to unexpected behaviors. This is especially true when following a pattern of starting with a general rule and following it with multiple exceptions or specific contradictory use cases. Adding specific rules and exceptions can cause conflict with the primary instruction and, potentially, with each other. What might seem like a minor modification can unexpectedly impact other aspects of a prompt. This is not only true when adding a new rule but also when adding more detail to an existing rule, like changing the order of the set of instructions or even simply rewording it. These minor modifications can unintentionally change the way the model interprets and prioritizes the set of instructions. The more details you add to a prompt, the greater the risk of unintended side effects. By trying to give too many details to every aspect of your task, you increase as well the risk of getting unexpected or deformed results. It is, therefore, essential to find the right balance between clarity and a high level of specification to maximise the relevance and consistency of the response. At a certain point, fixing one requirement can break two others, creating the frustrating feeling of taking one step forward and two steps backward in the optimization process. Testing each change manually becomes quickly overwhelming. This is especially true when one needs to optimize prompts that must follow numerous competing specifications in a complex AI task. The process cannot simply be about modifying the prompt for one requirement after the other, hoping the previous instruction remains unaffected. It also can’t be a system of selecting examples and checking them by hand. A better process with a more scientific approach should focus on ensuring repeatability and reliability in prompt optimization. From laboratory to AI: Why testing LLM responses requires multiple iterations Science teaches us to use replicates to ensure reproducibility and build confidence in an experiment’s results. I have been working in academic research in chemistry and biology for more than a decade. In those fields, experimental results can be influenced by a multitude of factors that can lead to significant variability. To ensure the reliability and reproducibility of experimental results, scientists mostly employ a method known as triplicates. This approach involves conducting the same experiment three times under identical conditions, allowing the experimental variations to be of minor importance in the result. Statistical analysis (standard mean and deviation) conducted on the results, mostly in biology, allows the author of an experiment to determine the consistency of the results and strengthens confidence in the findings. Just like in biology and chemistry, this approach can be used with LLMs to achieve reliable responses. With LLMs, the generation of responses is non-deterministic, meaning that the same input can lead to different outputs due to the probabilistic nature of the models. This variability is challenging when evaluating the reliability and consistency of LLM outputs. In the same way that biological/chemical experiments require triplicates to ensure reproducibility, testing LLMs should need multiple iterations to measure reproducibility. A single test by use case is, therefore, not sufficient because it does not represent the inherent variability in LLM responses. At least five iterations per use case allow for a better assessment. By analyzing the consistency of the responses across these iterations, one can better evaluate the reliability of the model and identify any potential issues or variation. It ensures that the output of the model is correctly controlled. Multiply this across 10 to 15 different prompt requirements, and one can easily understand how, without a structured testing approach, we end up spending time in trial-and-error testing with no efficient way to assess quality. A systematic approach: Functional testing for prompt optimization To address these challenges, a structured evaluation methodology can be used to ease and accelerate the testing process and enhance the reliability of LLM outputs. This approach has several key components: Data fixtures: The approach’s core center is the data fixtures, which are composed of predefined input-output pairs specifically created for prompt testing. These fixtures serve as controlled scenarios that represent the various requirements and edge cases the LLM must handle. By using a diverse set of fixtures, the performance of the prompt can be evaluated efficiently across different conditions. Automated test validation: This approach automates the validation of the requirements on a set of data fixtures by comparison between the expected outputs defined in the fixtures and the LLM response. This automated comparison ensures consistency and reduces the potential for human error or bias in the evaluation process. It allows for quick identification of discrepancies, enabling fine and efficient prompt adjustments. Multiple iterations: To assess the inherent variability of the LLM responses, this method runs multiple iterations for each test case. This iterative approach mimics the triplicate method used in biological/chemical experiments, providing a more robust dataset for analysis. By observing the consistency of responses across iterations, we can better assess the stability and reliability of the prompt. Algorithmic scoring: The results of each test case are scored algorithmically, reducing the need for long and laborious « human » evaluation. This scoring system is designed to be objective and quantitative, providing clear metrics for assessing the performance of the prompt. And by focusing on measurable outcomes, we can make data-driven decisions to optimize the prompt effectively. Step 1: Defining test data fixtures Selecting or creating compatible test data fixtures is the most challenging step of our systematic approach because it requires careful thought. A fixture is not only any input-output pair; it must be crafted meticulously to evaluate the most accurate as possible performance of the LLM for a specific requirement. This process requires: 1. A deep understanding of the task and the behavior of the model to make sure the selected examples effectively test the expected output while minimizing ambiguity or bias. 2. Foresight into how the evaluation will be conducted algorithmically during the test. The quality of a fixture, therefore, depends not only on the good representativeness of the example but also on ensuring it can be efficiently tested algorithmically. A fixture consists of: • Input example: This is the data that will be given to the LLM for processing. It should represent a typical or edge-case scenario that the LLM is expected to handle. The input should be designed to cover a wide range of possible variations that the LLM might have to deal with in production. • Expected output: This is the expected result that the LLM should produce with the provided input example. It is used for comparison with the actual LLM response output during validation. Step 2: Running automated tests Once the test data fixtures are defined, the next step involves the execution of automated tests to systematically evaluate the performance of the LLM response on the selected use cases. As previously stated, this process makes sure that the prompt is thoroughly tested against various scenarios, providing a reliable evaluation of its efficiency. Execution process 1. Multiple iterations: For each test use case, the same input is provided to the LLM multiple times. A simple for loop in nb_iter with nb_iter = 5 and voila! 2. Response comparison: After each iteration, the LLM response is compared to the expected output of the fixture. This comparison checks whether the LLM has correctly processed the input according to the specified requirements. 3. Scoring mechanism: Each comparison results in a score: ◦ Pass (1): The response matches the expected output, indicating that the LLM has correctly handled the input. ◦ Fail (0): The response does not match the expected output, signaling a discrepancy that needs to be fixed. 4. Final score calculation: The scores from all iterations are aggregated to calculate the overall final score. This score represents the proportion of successful responses out of the total number of iterations. A high score, of course, indicates high prompt performance and reliability. Example: Removing author signatures from an article Let’s consider a simple scenario where an AI task is to remove author signatures from an article. To efficiently test this functionality, we need a set of fixtures that represent the various signature styles. A dataset for this example could be: Example InputExpected OutputA long articleJean LeblancThe long articleA long articleP. W. HartigThe long articleA long articleMCZThe long article Validation process: Signature removal check: The validation function checks if the signature is absent from the rewritten text. This is easily done programmatically by searching for the signature needle in the haystack output text. Test failure criteria: If the signature is still in the output, the test fails. This indicates that the LLM did not correctly remove the signature and that further adjustments to the prompt are required. If it is not, the test is passed. The test evaluation provides a final score that allows a data-driven assessment of the prompt efficiency. If it scores perfectly, there is no need for further optimization. However, in most cases, you will not get a perfect score because either the consistency of the LLM response to a case is low (for example, 3 out of 5 iterations scored positive) or there are edge cases that the model struggles with (0 out of 5 iterations). The feedback clearly indicates that there is still room for further improvements and it guides you to reexamine your prompt for ambiguous phrasing, conflicting rules, or edge cases. By continuously monitoring your score alongside your prompt modifications, you can incrementally reduce side effects, achieve greater efficiency and consistency, and approach an optimal and reliable output. A perfect score is, however, not always achievable with the selected model. Changing the model might just fix the situation. If it doesn’t, you know the limitations of your system and can take this fact into account in your workflow. With luck, this situation might just be solved in the near future with a simple model update. Benefits of this method Reliability of the result: Running five to ten iterations provides reliable statistics on the performance of the prompt. A single test run may succeed once but not twice, and consistent success for multiple iterations indicates a robust and well-optimized prompt. Efficiency of the process: Unlike traditional scientific experiments that may take weeks or months to replicate, automated testing of LLMs can be carried out quickly. By setting a high number of iterations and waiting for a few minutes, we can obtain a high-quality, reproducible evaluation of the prompt efficiency. Data-driven optimization: The score obtained from these tests provides a data-driven assessment of the prompt’s ability to meet requirements, allowing targeted improvements. Side-by-side evaluation: Structured testing allows for an easy assessment of prompt versions. By comparing the test results, one can identify the most effective set of parameters for the instructions (phrasing, order of instructions) to achieve the desired results. Quick iterative improvement: The ability to quickly test and iterate prompts is a real advantage to carefully construct the prompt ensuring that the previously validated requirements remain as the prompt increases in complexity and length. By adopting this automated testing approach, we can systematically evaluate and enhance prompt performance, ensuring consistent and reliable outputs with the desired requirements. This method saves time and provides a robust analytical tool for continuous prompt optimization. Systematic prompt testing: Beyond prompt optimization Implementing a systematic prompt testing approach offers more advantages than just the initial prompt optimization. This methodology is valuable for other aspects of AI tasks: 1. Model comparison: ◦ Provider evaluation: This approach allows the efficient comparison of different LLM providers, such as ChatGPT, Claude, Gemini, Mistral, etc., on the same tasks. It becomes easy to evaluate which model performs the best for their specific needs. ◦ Model version: State-of-the-art model versions are not always necessary when a prompt is well-optimized, even for complex AI tasks. A lightweight, faster version can provide the same results with a faster response. This approach allows a side-by-side comparison of the different versions of a model, such as Gemini 1.5 flash vs. 1.5 pro vs. 2.0 flash or ChatGPT 3.5 vs. 4o mini vs. 4o, and allows the data-driven selection of the model version. 2. Version upgrades: ◦ Compatibility verification: When a new model version is released, systematic prompt testing helps validate if the upgrade maintains or improves the prompt performance. This is crucial for ensuring that updates do not unintentionally break the functionality. ◦ Seamless Transitions: By identifying key requirements and testing them, this method can facilitate better transitions to new model versions, allowing fast adjustment when necessary in order to maintain high-quality outputs. 3. Cost optimization: ◦ Performance-to-cost ratio: Systematic prompt testing helps in choosing the best cost-effective model based on the performance-to-cost ratio. We can efficiently identify the most efficient option between performance and operational costs to get the best return on LLM costs. Overcoming the challenges The biggest challenge of this approach is the preparation of the set of test data fixtures, but the effort invested in this process will pay off significantly as time passes. Well-prepared fixtures save considerable debugging time and enhance model efficiency and reliability by providing a robust foundation for evaluating the LLM response. The initial investment is quickly returned by improved efficiency and effectiveness in LLM development and deployment. Quick pros and cons Key advantages: Continuous improvement: The ability to add more requirements over time while ensuring existing functionality stays intact is a significant advantage. This allows for the evolution of the AI task in response to new requirements, ensuring that the system remains up-to-date and efficient. Better maintenance: This approach enables the easy validation of prompt performance with LLM updates. This is crucial for maintaining high standards of quality and reliability, as updates can sometimes introduce unintended changes in behavior. More flexibility: With a set of quality control tests, switching LLM providers becomes more straightforward. This flexibility allows us to adapt to changes in the market or technological advancements, ensuring we can always use the best tool for the job. Cost optimization: Data-driven evaluations enable better decisions on performance-to-cost ratio. By understanding the performance gains of different models, we can choose the most cost-effective solution that meets the needs. Time savings: Systematic evaluations provide quick feedback, reducing the need for manual testing. This efficiency allows to quickly iterate on prompt improvement and optimization, accelerating the development process. Challenges Initial time investment: Creating test fixtures and evaluation functions can require a significant investment of time. Defining measurable validation criteria: Not all AI tasks have clear pass/fail conditions. Defining measurable criteria for validation can sometimes be challenging, especially for tasks that involve subjective or nuanced outputs. This requires careful consideration and may involve a difficult selection of the evaluation metrics. Cost associated with multiple tests: Multiple test use cases associated with 5 to 10 iterations can generate a high number of LLM requests for a single test automation. But if the cost of a single LLM call is neglectable, as it is in most cases for text input/output calls, the overall cost of a test remains minimal. Conclusion: When should you implement this approach? Implementing this systematic testing approach is, of course, not always necessary, especially for simple tasks. However, for complex AI workflows in which precision and reliability are critical, this approach becomes highly valuable by offering a systematic way to assess and optimize prompt performance, preventing endless cycles of trial and error. By incorporating functional testing principles into Prompt Engineering, we transform a traditionally subjective and fragile process into one that is measurable, scalable, and robust. Not only does it enhance the reliability of LLM outputs, it helps achieve continuous improvement and efficient resource allocation. The decision to implement systematic prompt Testing should be based on the complexity of your project. For scenarios demanding high precision and consistency, investing the time to set up this methodology can significantly improve outcomes and speed up the development processes. However, for simpler tasks, a more classical, lightweight approach may be sufficient. The key is to balance the need for rigor with practical considerations, ensuring that your testing strategy aligns with your goals and constraints. Thanks for reading!

If you have ever felt trapped in an endless loop of trial and error, adjusting one rule only to see another one fail, you’re not alone! The reality is that traditional prompt optimisation is clearly missing a structured, more scientific approach that will help to ensure reliability.

That’s where functional testing for prompt engineering comes in! This approach, inspired by methodologies of experimental science, leverages automated input-output testing with multiple iterations and algorithmic scoring to turn prompt engineering into a measurable, data-driven process.

No more guesswork. No more tedious manual validation. Just precise and repeatable results that allow you to fine-tune prompts efficiently and confidently.

In this article, we will explore a systematic approach for mastering prompt engineering, which ensures your Llm outputs will be efficient and reliable even for the most complex AI tasks.

Balancing precision and consistency in prompt optimisation

Adding a large set of rules to a prompt can introduce partial contradictions between rules and lead to unexpected behaviors. This is especially true when following a pattern of starting with a general rule and following it with multiple exceptions or specific contradictory use cases. Adding specific rules and exceptions can cause conflict with the primary instruction and, potentially, with each other.

What might seem like a minor modification can unexpectedly impact other aspects of a prompt. This is not only true when adding a new rule but also when adding more detail to an existing rule, like changing the order of the set of instructions or even simply rewording it. These minor modifications can unintentionally change the way the model interprets and prioritizes the set of instructions.

The more details you add to a prompt, the greater the risk of unintended side effects. By trying to give too many details to every aspect of your task, you increase as well the risk of getting unexpected or deformed results. It is, therefore, essential to find the right balance between clarity and a high level of specification to maximise the relevance and consistency of the response. At a certain point, fixing one requirement can break two others, creating the frustrating feeling of taking one step forward and two steps backward in the optimization process.

Testing each change manually becomes quickly overwhelming. This is especially true when one needs to optimize prompts that must follow numerous competing specifications in a complex AI task. The process cannot simply be about modifying the prompt for one requirement after the other, hoping the previous instruction remains unaffected. It also can’t be a system of selecting examples and checking them by hand. A better process with a more scientific approach should focus on ensuring repeatability and reliability in prompt optimization.

From laboratory to AI: Why testing LLM responses requires multiple iterations

Science teaches us to use replicates to ensure reproducibility and build confidence in an experiment’s results. I have been working in academic research in chemistry and biology for more than a decade. In those fields, experimental results can be influenced by a multitude of factors that can lead to significant variability. To ensure the reliability and reproducibility of experimental results, scientists mostly employ a method known as triplicates. This approach involves conducting the same experiment three times under identical conditions, allowing the experimental variations to be of minor importance in the result. Statistical analysis (standard mean and deviation) conducted on the results, mostly in biology, allows the author of an experiment to determine the consistency of the results and strengthens confidence in the findings.

Just like in biology and chemistry, this approach can be used with LLMs to achieve reliable responses. With LLMs, the generation of responses is non-deterministic, meaning that the same input can lead to different outputs due to the probabilistic nature of the models. This variability is challenging when evaluating the reliability and consistency of LLM outputs.

In the same way that biological/chemical experiments require triplicates to ensure reproducibility, testing LLMs should need multiple iterations to measure reproducibility. A single test by use case is, therefore, not sufficient because it does not represent the inherent variability in LLM responses. At least five iterations per use case allow for a better assessment. By analyzing the consistency of the responses across these iterations, one can better evaluate the reliability of the model and identify any potential issues or variation. It ensures that the output of the model is correctly controlled.

Multiply this across 10 to 15 different prompt requirements, and one can easily understand how, without a structured testing approach, we end up spending time in trial-and-error testing with no efficient way to assess quality.

A systematic approach: Functional testing for prompt optimization

To address these challenges, a structured evaluation methodology can be used to ease and accelerate the testing process and enhance the reliability of LLM outputs. This approach has several key components:

Data fixtures: The approach’s core center is the data fixtures, which are composed of predefined input-output pairs specifically created for prompt testing. These fixtures serve as controlled scenarios that represent the various requirements and edge cases the LLM must handle. By using a diverse set of fixtures, the performance of the prompt can be evaluated efficiently across different conditions.
Automated test validation: This approach automates the validation of the requirements on a set of data fixtures by comparison between the expected outputs defined in the fixtures and the LLM response. This automated comparison ensures consistency and reduces the potential for human error or bias in the evaluation process. It allows for quick identification of discrepancies, enabling fine and efficient prompt adjustments.
Multiple iterations: To assess the inherent variability of the LLM responses, this method runs multiple iterations for each test case. This iterative approach mimics the triplicate method used in biological/chemical experiments, providing a more robust dataset for analysis. By observing the consistency of responses across iterations, we can better assess the stability and reliability of the prompt.
Algorithmic scoring: The results of each test case are scored algorithmically, reducing the need for long and laborious « human » evaluation. This scoring system is designed to be objective and quantitative, providing clear metrics for assessing the performance of the prompt. And by focusing on measurable outcomes, we can make data-driven decisions to optimize the prompt effectively.

Step 1: Defining test data fixtures

Selecting or creating compatible test data fixtures is the most challenging step of our systematic approach because it requires careful thought. A fixture is not only any input-output pair; it must be crafted meticulously to evaluate the most accurate as possible performance of the LLM for a specific requirement. This process requires:

1. A deep understanding of the task and the behavior of the model to make sure the selected examples effectively test the expected output while minimizing ambiguity or bias.

2. Foresight into how the evaluation will be conducted algorithmically during the test.

The quality of a fixture, therefore, depends not only on the good representativeness of the example but also on ensuring it can be efficiently tested algorithmically.

A fixture consists of:

• Input example: This is the data that will be given to the LLM for processing. It should represent a typical or edge-case scenario that the LLM is expected to handle. The input should be designed to cover a wide range of possible variations that the LLM might have to deal with in production.

• Expected output: This is the expected result that the LLM should produce with the provided input example. It is used for comparison with the actual LLM response output during validation.

Step 2: Running automated tests

Once the test data fixtures are defined, the next step involves the execution of automated tests to systematically evaluate the performance of the LLM response on the selected use cases. As previously stated, this process makes sure that the prompt is thoroughly tested against various scenarios, providing a reliable evaluation of its efficiency.

Execution process

1. Multiple iterations: For each test use case, the same input is provided to the LLM multiple times. A simple for loop in nb_iter with nb_iter = 5 and voila!

2. Response comparison: After each iteration, the LLM response is compared to the expected output of the fixture. This comparison checks whether the LLM has correctly processed the input according to the specified requirements.

3. Scoring mechanism: Each comparison results in a score:

◦ Pass (1): The response matches the expected output, indicating that the LLM has correctly handled the input.

◦ Fail (0): The response does not match the expected output, signaling a discrepancy that needs to be fixed.

4. Final score calculation: The scores from all iterations are aggregated to calculate the overall final score. This score represents the proportion of successful responses out of the total number of iterations. A high score, of course, indicates high prompt performance and reliability.

Example: Removing author signatures from an article

Let’s consider a simple scenario where an AI task is to remove author signatures from an article. To efficiently test this functionality, we need a set of fixtures that represent the various signature styles.

A dataset for this example could be:

Example Input	Expected Output
A long article Jean Leblanc	The long article
A long article P. W. Hartig	The long article
A long article MCZ	The long article

Validation process:

Signature removal check: The validation function checks if the signature is absent from the rewritten text. This is easily done programmatically by searching for the signature needle in the haystack output text.
Test failure criteria: If the signature is still in the output, the test fails. This indicates that the LLM did not correctly remove the signature and that further adjustments to the prompt are required. If it is not, the test is passed.

The test evaluation provides a final score that allows a data-driven assessment of the prompt efficiency. If it scores perfectly, there is no need for further optimization. However, in most cases, you will not get a perfect score because either the consistency of the LLM response to a case is low (for example, 3 out of 5 iterations scored positive) or there are edge cases that the model struggles with (0 out of 5 iterations).

The feedback clearly indicates that there is still room for further improvements and it guides you to reexamine your prompt for ambiguous phrasing, conflicting rules, or edge cases. By continuously monitoring your score alongside your prompt modifications, you can incrementally reduce side effects, achieve greater efficiency and consistency, and approach an optimal and reliable output.

A perfect score is, however, not always achievable with the selected model. Changing the model might just fix the situation. If it doesn’t, you know the limitations of your system and can take this fact into account in your workflow. With luck, this situation might just be solved in the near future with a simple model update.

Benefits of this method

Reliability of the result: Running five to ten iterations provides reliable statistics on the performance of the prompt. A single test run may succeed once but not twice, and consistent success for multiple iterations indicates a robust and well-optimized prompt.
Efficiency of the process: Unlike traditional scientific experiments that may take weeks or months to replicate, automated testing of LLMs can be carried out quickly. By setting a high number of iterations and waiting for a few minutes, we can obtain a high-quality, reproducible evaluation of the prompt efficiency.
Data-driven optimization: The score obtained from these tests provides a data-driven assessment of the prompt’s ability to meet requirements, allowing targeted improvements.
Side-by-side evaluation: Structured testing allows for an easy assessment of prompt versions. By comparing the test results, one can identify the most effective set of parameters for the instructions (phrasing, order of instructions) to achieve the desired results.
Quick iterative improvement: The ability to quickly test and iterate prompts is a real advantage to carefully construct the prompt ensuring that the previously validated requirements remain as the prompt increases in complexity and length.

By adopting this automated testing approach, we can systematically evaluate and enhance prompt performance, ensuring consistent and reliable outputs with the desired requirements. This method saves time and provides a robust analytical tool for continuous prompt optimization.

Systematic prompt testing: Beyond prompt optimization

Implementing a systematic prompt testing approach offers more advantages than just the initial prompt optimization. This methodology is valuable for other aspects of AI tasks:

1. Model comparison:

◦ Provider evaluation: This approach allows the efficient comparison of different LLM providers, such as ChatGPT, Claude, Gemini, Mistral, etc., on the same tasks. It becomes easy to evaluate which model performs the best for their specific needs.

◦ Model version: State-of-the-art model versions are not always necessary when a prompt is well-optimized, even for complex AI tasks. A lightweight, faster version can provide the same results with a faster response. This approach allows a side-by-side comparison of the different versions of a model, such as Gemini 1.5 flash vs. 1.5 pro vs. 2.0 flash or ChatGPT 3.5 vs. 4o mini vs. 4o, and allows the data-driven selection of the model version.

2. Version upgrades:

◦ Compatibility verification: When a new model version is released, systematic prompt testing helps validate if the upgrade maintains or improves the prompt performance. This is crucial for ensuring that updates do not unintentionally break the functionality.

◦ Seamless Transitions: By identifying key requirements and testing them, this method can facilitate better transitions to new model versions, allowing fast adjustment when necessary in order to maintain high-quality outputs.

3. Cost optimization:

◦ Performance-to-cost ratio: Systematic prompt testing helps in choosing the best cost-effective model based on the performance-to-cost ratio. We can efficiently identify the most efficient option between performance and operational costs to get the best return on LLM costs.

Overcoming the challenges

The biggest challenge of this approach is the preparation of the set of test data fixtures, but the effort invested in this process will pay off significantly as time passes. Well-prepared fixtures save considerable debugging time and enhance model efficiency and reliability by providing a robust foundation for evaluating the LLM response. The initial investment is quickly returned by improved efficiency and effectiveness in LLM development and deployment.

Quick pros and cons

Key advantages:

Continuous improvement: The ability to add more requirements over time while ensuring existing functionality stays intact is a significant advantage. This allows for the evolution of the AI task in response to new requirements, ensuring that the system remains up-to-date and efficient.
Better maintenance: This approach enables the easy validation of prompt performance with LLM updates. This is crucial for maintaining high standards of quality and reliability, as updates can sometimes introduce unintended changes in behavior.
More flexibility: With a set of quality control tests, switching LLM providers becomes more straightforward. This flexibility allows us to adapt to changes in the market or technological advancements, ensuring we can always use the best tool for the job.
Cost optimization: Data-driven evaluations enable better decisions on performance-to-cost ratio. By understanding the performance gains of different models, we can choose the most cost-effective solution that meets the needs.
Time savings: Systematic evaluations provide quick feedback, reducing the need for manual testing. This efficiency allows to quickly iterate on prompt improvement and optimization, accelerating the development process.

Challenges

Initial time investment: Creating test fixtures and evaluation functions can require a significant investment of time.
Defining measurable validation criteria: Not all AI tasks have clear pass/fail conditions. Defining measurable criteria for validation can sometimes be challenging, especially for tasks that involve subjective or nuanced outputs. This requires careful consideration and may involve a difficult selection of the evaluation metrics.
Cost associated with multiple tests: Multiple test use cases associated with 5 to 10 iterations can generate a high number of LLM requests for a single test automation. But if the cost of a single LLM call is neglectable, as it is in most cases for text input/output calls, the overall cost of a test remains minimal.

Conclusion: When should you implement this approach?

Implementing this systematic testing approach is, of course, not always necessary, especially for simple tasks. However, for complex AI workflows in which precision and reliability are critical, this approach becomes highly valuable by offering a systematic way to assess and optimize prompt performance, preventing endless cycles of trial and error.

By incorporating functional testing principles into Prompt Engineering, we transform a traditionally subjective and fragile process into one that is measurable, scalable, and robust. Not only does it enhance the reliability of LLM outputs, it helps achieve continuous improvement and efficient resource allocation.

The decision to implement systematic prompt Testing should be based on the complexity of your project. For scenarios demanding high precision and consistency, investing the time to set up this methodology can significantly improve outcomes and speed up the development processes. However, for simpler tasks, a more classical, lightweight approach may be sufficient. The key is to balance the need for rigor with practical considerations, ensuring that your testing strategy aligns with your goals and constraints.

Thanks for reading!

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Qatar began supplying natural gas to Syria through Jordan, the latest boost to the war-torn country’s interim government following the fall of former president Bashar al-Assad. About 2 million cubic meters a day will be sent via the Arab Gas Pipeline, eventually contributing a total of 400 megawatts to the power grid, Syrian state-run news agency Sana said. The supplies were approved by Washington, Reuters reported earlier, without providing numbers. The contract signals further recognition for the government of Ahmed Al-Sharaa, who led the battle to overthrow Assad. It should help increase average power supply for Syrians to four hours a day, up from two, helping ease severe energy shortages. The UK removed the Syrian central bank and 23 other entities, mainly lenders and energy companies, from a list of sanctioned institutions earlier this month, following similar moves by several Western countries. Natural gas supplies through the Arab Gas Pipeline to Syria, and by extension to Lebanon, have been disrupted since 2011 due to the war and have been largely inactive since then. The exact mechanism by which Qatar will transport the gas to Syria and reactivate that section of the pipeline is unclear, as years of conflict have damaged vital energy infrastructure. Plus, the only LNG storage facility in Jordan, a vessel off the Red Sea port city of Aqaba, will be leased to Egypt for 10 years starting mid-2025. The power supply hinges on raising the production capacity of Syria’s Deir Ali power station, state-run Qatar News Agency said. This supply level is the “first phase” of a deal signed between Qatar Fund for Development and the Jordanian Ministry of Energy, in cooperation with the United Nations Development Program, which will oversee the “executive aspects of the project”. Syria’s interim government is seeking to replace oil imports from

Energy Bosses Shrug Off DeepSeek to Focus on Powering AI Boom

While tariffs and macroeconomic concerns weighed on the outlook for oil at a major energy conference in Houston this week, the mood around artificial intelligence and its sky-high power needs could scarcely be different. For a second year, energy executives at the CERAWeek by S&P Global gathering hailed the looming data center requirements for AI as both a huge challenge and a once-in-a-generation opportunity. “The only way we win the AI arms race with China is if we have electricity,” US Interior Secretary Doug Burgum said in his address. “They are moving at a speed that would suggest we are in a serious cyberwar with them.” The energy world appears to have shrugged off investor doubts that emerged over the AI-power narrative in January, when Chinese startup DeepSeek released a chat bot purported to use just a fraction of the electricity required by established US rivals. Despite that wobble, many forecasts for US power demand are still unprecedented — and come after more than two decades of stable consumption. Jenny Yang, head of power and renewables research at S&P, told conference delegates Thursday that US utilities’ estimates for additional power demand coming just from data centers by 2030 are equivalent to the entire Ercot power market in Texas. “We’re seeing load forecasts that, in my experience as a state regulator, are mind-boggling,” said Mark Christie, a former energy regulator in Virginia, the data-center capital of the US, and who now chairs the Federal Energy Regulatory Commission. The so-called hyperscalers continue to race ahead with their build-out of AI infrastructure. Google parent Alphabet Inc. reported last month it plans capital expenditures of $75 billion this year. The power demand related to that spending “is coming so fast and from so many different directions,” Alan Armstrong, chief executive officer of US pipeline operator Williams

The Emperor’s New Clothes: BP and Shell’s duck diplomacy

BP’s (LON:BP) undressing of its energy transition goals is the latest and most significant example of an oil supermajor reneging on its green investment pledges. It is easy to speculate that companies such as BP, and similarly Shell (LON:SHEL), have attempted to diversify into renewable energy too quickly. However, diversification in the energy transition could be the very thing that pulls the cart out of danger. This week, BP’s chief executive Murray Auchincloss defended the company’s decision to jettison renewable energy pledges and increase oil and gas production. In late February, he said the oil major had accelerated “too far, too fast” in the transition to renewable energy. “Our optimism for a fast transition was misplaced,” he said, after profits fell across its low-carbon and gas division, precipitating a sudden strategic about-face. The company, which has been under pressure from analysts and shareholders to reduce its low-carbon investments and double down on its core business of oil and gas, plans to cut investment in low-carbon projects by $5 billion (£4bn), Auchincloss said. © Image: BloombergLondon’s Old Oil Stocks Diverge | BP underperforms Shell on worries about green transition, payouts. “The challenge that faces BP and Equinor, and to varying degrees Shell and Equinor, is the marked underperformance of their shares relative to that of their US peers,” says Russ Mould, investment director at AJ Bell. “Whether this is down to the relatively greater emphasis they have placed upon investment in renewables to facilitate a move away from hydrocarbons or simply down to their stock market domicile (given how US equities continue to dominate across the board) is hard to divine, but the truth may well lie somewhere between. There is a sense that shareholders are becoming restless.” BP’s shares have shown a marked underperformance relative to global peers since former

Peterhead’s Acorn CCS key to unlocking future of Grangemouth

Grangemouth will need the Acorn Carbon Capture and Storage (CCS) development to go ahead to take full advantage of the upcoming £13 billion Project Willow plan. Colin Pritchard, sustainability and external relations director at Ineos, which runs the Grangemouth refinery Petroineos in a joint venture with PetroChina, said: “If you want to really go for all of the things that are within Willow and take them to the full extent, you will need a CO2 transportation and storage system. “In that case, the full extent of Willow needs Acorn.” Project Willow is the plan currently being developed by the UK and Scottish Governments to ameliorate the closure of the Scotland’s only oil refinery with the expected loss of 400 jobs. Due for release soon, Project Willow will lay out nine potential projects to overhaul the Grangemouth refinery in Scotland and create a long-term sustainable future for the site. A feasibility study exploring options for overhauling the Grangemouth refinery in Scotland is reportedly set to propose £3.8bn of investments in low-carbon alternatives for the site over ten years, with a best-case scenario could see the amount rise to almost £13bn. These options include recycling plastics, the production of biomethane, sustainable aviation fuel (SAF) and renewable diesel. In turn, these are hoped to avert the shutdown of Grangemouth, scheduled for the second quarter of this year, and preserve jobs at the facility. Speaking to Energy Voice on the side-lines of the DeCarbScotland event, Pritchard added: “There are some projects there are not dependent on Acorn, but there are some projects within Willow, like e-methanol, which are.” He added that the nine projects envisioned in Project Willow are an initial project set and could evolve, making CCS essential “if you want to get the full benefit of what we put in Willow”. Based in

EIA Reveals Latest Brent Oil Price Forecast for 2025 and 2026

The U.S. Energy Information Administration (EIA) has revealed its latest Brent spot price forecast for 2025 and 2026 in its March Short Term Energy Outlook (STEO), which was released this week. According to the STEO, the EIA now sees the Brent spot price averaging $74.22 per barrel this year and $68.47 per barrel next year. In its previous STEO, which was released in February, the EIA projected that the Brent spot price would average $74.50 per barrel in 2025 and $66.46 per barrel in 2026. The EIA outlined in its latest STEO that it sees the Brent spot price coming in at $74.89 per barrel in the first quarter of this year, $74.00 per barrel in the second quarter, $75.00 per barrel in the third quarter, $73.02 per barrel in the fourth quarter, $71.00 per barrel in the first quarter of 2026, $69.00 per barrel in the second quarter, $68.00 per barrel in the third quarter, and $66.00 per barrel in the fourth quarter. In its previous February STEO, the EIA forecast that the Brent spot price would average $77.13 per barrel in the first quarter of 2025, $75.00 per barrel in the second quarter, $74.00 per barrel in the third quarter, $72.00 per barrel in the fourth quarter, $68.97 per barrel in the first quarter of 2026, $67.33 per barrel in the second quarter, $65.68 per barrel in the third quarter, and $64.00 per barrel in the fourth quarter of next year. In its latest STEO, the EIA highlighted that the Brent crude oil spot price averaged $75 per barrel in February, which it pointed out was $4 per barrel lower than in January and $8 per barrel lower than at the same time last year. “Crude oil prices fell during February driven largely by economic growth concerns related

IBM laying foundation for mainframe as ultimate AI server

“It will truly change what customers are able to do with AI,” Stowell said. IBM’s mainframe processors The next generation of processors is expected to continue a long history of generation-to-generation improvements, IBM stated in a new white paper on AI and the mainframe. “They are projected to clock in at 5.5 GHz. and include ten 36 MB level 2 caches. They’ll feature built-in low-latency data processing for accelerated I/O as well as a completely redesigned cache and chip-interconnection infrastructure for more on-chip cache and compute capacity,” IBM wrote. Today’s mainframes also have extensions and accelerators that integrate with the core systems. These specialized add-ons are designed to enable the adoption of technologies such as Java, cloud and AI by accelerating computing paradigms that are essential for high-volume, low-latency transaction processing, IBM wrote. “The next crop of AI accelerators are expected to be significantly enhanced—with each accelerator designed to deliver 4 times more compute power, reaching 24 trillion operations per second (TOPS),” IBM wrote. “The I/O and cache improvements will enable even faster processing and analysis of large amounts of data and consolidation of workloads running across multiple servers, for savings in data center space and power costs. And the new accelerators will provide increased capacity to enable additional transaction clock time to perform enhanced in-transaction AI inferencing.” In addition, the next generation of the accelerator architecture is expected to be more efficient for AI tasks. “Unlike standard CPUs, the chip architecture will have a simpler layout, designed to send data directly from one compute engine, and use a range of lower- precision numeric formats. These enhancements are expected to make running AI models more energy efficient and far less memory intensive. As a result, mainframe users can leverage much more complex AI models and perform AI inferencing at a greater scale

VergeIO enhances VergeFabric network virtualization offering

VergeIO is not, however, using an off-the-shelf version of KVM. Rather, it is using what Crump referred to as a heavily modified KVM hypervisor base, with significant proprietary enhancements while still maintaining connections to the open-source community. VergeIO’s deployment profile is currently 70% on premises and about 30% via bare-metal service providers, with a particularly strong following among cloud service providers that host applications for their customers. The software requires direct hardware access due to its low-level integration with physical resources. “Since November of 2023, the normal number one customer we’re attracting right now is guys that have had a heart attack when they got their VMware renewal license,” Crump said. “The more of the stack you own, the better our story becomes.” A 2024 report from Data Center Intelligence Group (DCIG) identified VergeOS as one of the top 5 alternatives to VMware. “VergeIO starts by installing VergeOS on bare metal servers,” the report stated. “It then brings the servers’ hardware resources under its management, catalogs these resources, and makes them available to VMs. By directly accessing and managing the server’s hardware resources, it optimizes them in ways other hypervisors often cannot.” Advanced networking features in VergeFabric VergeFabric is the networking component within the VergeOS ecosystem, providing software-defined networking capabilities as an integrated service rather than as a separate virtual machine or application.

Podcast: On the Frontier of Modular Edge AI Data Centers with Flexnode’s Andrew Lindsey

The modular data center industry is undergoing a seismic shift in the age of AI, and few are as deeply embedded in this transformation as Andrew Lindsey, Co-Founder and CEO of Flexnode. In a recent episode of the Data Center Frontier Show podcast, Lindsey joined Editor-in-Chief Matt Vincent and Senior Editor David Chernicoff to discuss the evolution of modular data centers, the growing demand for high-density liquid-cooled solutions, and the industry factors driving this momentum. A Background Rooted in Innovation Lindsey’s career has been defined by the intersection of technology and the built environment. Prior to launching Flexnode, he worked at Alpha Corporation, a top 100 engineering and construction management firm founded by his father in 1979. His early career involved spearheading technology adoption within the firm, with a focus on high-security infrastructure for both government and private clients. Recognizing a massive opportunity in the data center space, Lindsey saw a need for an innovative approach to infrastructure deployment. “The construction industry is relatively uninnovative,” he explained, citing a McKinsey study that ranked construction as the second least-digitized industry—just above fishing and wildlife, which remains deliberately undigitized. Given the billions of square feet of data center infrastructure required in a relatively short timeframe, Lindsey set out to streamline and modernize the process. Founded four years ago, Flexnode delivers modular data centers with a fully integrated approach, handling everything from site selection to design, engineering, manufacturing, deployment, operations, and even end-of-life decommissioning. Their core mission is to provide an “easy button” for high-density computing solutions, including cloud and dedicated GPU infrastructure, allowing faster and more efficient deployment of modular data centers. The Rising Momentum for Modular Data Centers As Vincent noted, Data Center Frontier has closely tracked the increasing traction of modular infrastructure. Lindsey has been at the forefront of this

Last Energy to Deploy 30 Microreactors in Texas for Data Centers

As the demand for data center power surges in Texas, nuclear startup Last Energy has now announced plans to build 30 microreactors in the state’s Haskell County near the Dallas-Fort Worth Metroplex. The reactors will serve a growing customer base of data center operators in the region looking for reliable, carbon-free energy. The plan marks Last Energy’s largest project to date and a significant step in advancing modular nuclear power as a viable solution for high-density computing infrastructure. Meeting the Looming Power Demands of Texas Data Centers Texas is already home to over 340 data centers, with significant expansion underway. Google is increasing its data center footprint in Dallas, while OpenAI’s Stargate has announced plans for a new facility in Abilene, just an hour south of Last Energy’s planned site. The company notes the Dallas-Fort Worth metro area alone is projected to require an additional 43 gigawatts of power in the coming years, far surpassing current grid capacity. To help remediate, Last Energy has secured a 200+ acre site in Haskell County, approximately three and a half hours west of Dallas. The company has also filed for a grid connection with ERCOT, with plans to deliver power via a mix of private wire and grid transmission. Additionally, Last Energy has begun pre-application engagement with the U.S. Nuclear Regulatory Commission (NRC) for an Early Site Permit, a key step in securing regulatory approval. According to Last Energy CEO Bret Kugelmass, the company’s modular approach is designed to bring nuclear energy online faster than traditional projects. “Nuclear power is the most effective way to meet Texas’ growing energy demand, but it needs to be deployed faster and at scale,” Kugelmass said. “Our microreactors are designed to be plug-and-play, enabling data center operators to bypass the constraints of an overloaded grid.” Scaling Nuclear for

Data Center Jobs: Engineering and Technician Jobs Available in Major Markets

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Data Center Facility Engineer (Night Shift Available) Ashburn, VAThis position is also available in: Tacoma, WA (Nights), Days/Nights: Needham, MA and New York City, NY. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OHThis traveling position is also available in: Somerset, NJ; Boydton, VA; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Des Moines, IA; San Jose, CA; Portland, OR; St Louis, MO; Phoenix, AZ; Dallas, TX; Chicago, IL; or Toronto, ON. *** ALSO looking for a LEAD EE and ME CxA agents.*** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Switchgear Field Service Technician – Critical Facilities Nationwide TravelThis position is also available in: Charlotte, NC; Atlanta, GA; Dallas,

Amid Shifting Regional Data Center Policies, Iron Mountain and DC Blox Both Expand in Virginia’s Henrico County

The dynamic landscape of data center developments in Maryland and Virginia exemplify the intricate balance between fostering technological growth and addressing community and environmental concerns. Data center developers in this region find themselves both in the crosshairs of groups worried about the environment and other groups looking to drive economic growth. In some cases, the groups are different components of the same organizations, such as local governments. For data center development, meeting the needs of these competing interests often means walking a none-too-stable tightrope. Rapid Government Action Encourages Growth In May 2024, Maryland demonstrated its commitment to attracting data center investments by enacting the Critical Infrastructure Streamlining Act. This legislation provides a clear framework for the use of emergency backup power generation, addressing previous regulatory challenges that a few months earlier had hindered projects like Aligned Data Centers’ proposed 264-megawatt campus in Frederick County, causing Aligned to pull out of the project. However, just days after the Act was signed by the governor, Aligned reiterated its plans to move forward with development in Maryland. With the Quantum Loop and the related data center development making Frederick County a focal point for a balanced approach, the industry is paying careful attention to the pace of development and the relations between developers, communities and the government. In September of 2024, Frederick County Executive Jessica Fitzwater revealed draft legislation that would potentially restrict where in the county data centers could be built. The legislation was based on information found in the Frederick County Data Centers Workgroup’s final report. Those bills would update existing regulations and create a floating zone for Critical Digital Infrastructure and place specific requirements on siting data centers. Statewide, a cautious approach to environmental and community impacts statewide has been deemed important. In January 2025, legislators introduced SB116, a bill

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle