New LLM versions may offer impressive gains in performance and lower costs, enticing teams to upgrade. However, these upgrades can silently break working features, often without warning.
Be wary of model upgrades that can change outputs enough to break existing workflows, prompts, and saved skills. LLM regression testing can help identify these issues before they become major problems. By running legacy and new models side by side, teams can assess the impact of the upgrade on their specific use cases.
Unfortunately, these changes can’t be predicted with certainty. Different models can process text in countless ways, and even the smallest tweak can have significant consequences. Test your workflow before upgrading to a new LLM version, and don’t be afraid to revisit and refine your evaluation dataset accordingly.
A smooth upgrade is often a matter of being prepared. By including LLM regression testing and ongoing workflow assessments in your development cycle, you can minimize the risk of disruption and ensure a seamless transition to the new model.
Treat Prompts like Production Code
Automated regression tests catch behavioral changes before a model upgrade reaches users.
Catch application regressions with tests before pushing changes live.
When updating an AI model, minor tweaks can sometimes have significant side effects. This is especially true for prompts or applications that rely heavily on the model’s output. Behavioral changes that seem insignificant in isolation can start to add up and cause problems down the line.
To catch these issues early on, it’s essential to have automated regression tests in place. These tests can simulate real-world interactions and identify any deviations from expected behavior. In this way, users can be alerted to potential problems before the model upgrade even goes live.
This isn’t just about preventing immediate disasters; it’s also an opportunity to refine and improve the overall experience. By catching regressions at the testing phase, developers can revisit the design and make intentional adjustments to mitigate any negative effects.
Don’t wait until it’s too late – set up automated regression tests to protect against the risks of unseen regressions. With this safety net in place, you can update your AI model with confidence, knowing that any potential issues will be caught and addressed before users are impacted.
Build a Representative Evaluation Dataset
To build a truly representative evaluation dataset, you need to capture realistic inputs and expected outcomes from your production workflows. This may involve collecting data on user interactions, system responses, and performance metrics.
Edge cases are just as important as regular scenarios. They often expose hidden issues, such as unusual requests, system failures, or security breaches. Your test dataset should include these cases to ensure your models are prepared for anything.
Save tasks that were previously successful so you can verify that new changes have not broken these areas. This ensures regression testing is thorough and catches all changes that might affect reliability.
Saving inputs and outcomes from production workflows, along with tasks that were previously successful, allows you to create a robust test dataset that is realistic and comprehensive. When you save these inputs and outcomes, you can make sure your model will perform as expected under various conditions, giving you confidence that it will behave correctly when put into production.
Compare Apples to Apples: Structured Output Comparison
When comparing structured outputs and tool calls, identifying regressions is crucial to ensure that changes don’t break downstream processes.
To do this reliably, it’s not enough to just compare exact text matches. Use policy compliance and task success metrics instead, which will give you a more comprehensive view of how well your system is performing.
Another challenge is snapshot behavior – the way your system behaves at different stages of its operations. To truly compare apples to apples, you need to snapshot behavior, not just exact wording.
This might involve creating a library of snapshots of how your system responds to various inputs and edge cases. This can then be used to compare against a new version of the system, to see if there’s any change in behavior that might indicate a regression.
LLM regression testing is essential to ensure that changes to language models don’t introduce new bugs or break existing functionality. By combining policy compliance and task success metrics with snapshot behavior, you can identify regressions with greater accuracy, and ensure that your language model continues to perform as expected.
This approach also helps you to identify subtle changes in behavior that might not be caught by a simple comparison of exact text matches. By looking at the broader picture, you can be more confident that your system is working as intended.
Run Legacy and New Models Side by Side
When upgrading to a new LLM version, the impact on your workflow and performance can be significant. One effective way to understand these changes is to evaluate the same test cases against both LLM versions.
This involves running your old and new models in parallel, side by side, to identify regressions and improvements. You’ll want to set up your evaluation environment to handle both models simultaneously, ensuring they’re receiving the same inputs and operating under the same conditions.
By comparing the performance of both models on the same test cases, you can pinpoint changes in latency and observe any significant variations in resource usage. This can be particularly helpful in determining whether the new model is worth the additional cost. Even if the new model improves performance in some areas, latency and cost differences may offset those gains.
Make Evaluations Part of CI/CD
In order to ensure high-quality results and prevent downstream issues, evaluations need to be woven into your continuous integration/continuous deployment (CI/CD) pipeline. This approach allows you to trigger targeted test suites when changes occur, guaranteeing that your models are performing optimally every step of the way.
However, many teams still release new models without thoroughly evaluating their performance. When releases are blocked solely based on missing critical thresholds in evaluations, it makes sense to catch issues early on through code-level testing instead.
Automating evaluations not only saves time but also helps teams catch problems at every stage of development. This ensures that any discrepancies or performance degradation are caught before they impact end-users.
