Do you write integration tests for your most common LLM prompts?

When integrating Azure AI's language models (LLMs) into your application, it’s important to ensure that the responses generated by the LLM are reliable and consistent. However, LLMs are non-deterministic, meaning the same prompt may not always generate the exact same response. This can introduce challenges in maintaining the quality of outputs in production environments. Writing integration tests for your most common LLM prompts helps you identify when model changes or updates could impact your application’s performance.

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Why you need integration tests for LLM prompts

Ensure consistency: Integration tests allow you to check if the responses for your most critical prompts stay within an acceptable range of variation. Without these tests, you risk introducing variability that could negatively affect user experience or critical business logic

Detect regressions early: As Azure AI models evolve and get updated, prompt behavior may change. By running tests regularly, you can catch regressions that result from model updates or changes in prompt design

Measure prompt quality: Integration tests help you evaluate the quality of your prompts over time by establishing benchmarks for acceptable responses. You can track if the output still meets your defined criteria

Test edge cases: Prompts can behave unpredictably with edge case inputs. By testing common and edge case scenarios, you can ensure your AI model handles these situations gracefully

Best practices for writing LLM integration tests

Identify critical prompts: Focus on writing tests for the most frequently used or mission-critical prompts in your application

Set output expectations: Define a range of acceptable output variations for your test cases. This might include specific keywords, response length, or adherence to format requirements

Automate testing: Use continuous integration (CI) pipelines to automatically run your LLM integration tests after each deployment or model update

Log outputs: Log the outputs from your tests to detect subtle changes over time. This can help identify patterns in model behavior and flag potential issues before they become problematic