Regression Testing
Testing to ensure that new changes, updates, or deployments don't break existing functionality or degrade AI performance.
Overview
Regression testing validates that your AI system maintains its quality and behavior after code changes, model updates, or configuration modifications. In LLM-based systems, regression testing is crucial because even small changes can have unexpected effects on behavior.
Why Regression Testing for AI?
Regressions in AI systems can stem from various changes: switching to a new model version, modifying system prompts or instructions, updating preprocessing or post-processing logic, adjusting configuration parameters like temperature or endpoints, or updating dependencies in underlying libraries and services.
Unlike traditional software, AI systems are non-deterministic—the same input may produce different outputs across runs. Quality degradation can be subtle and not immediately obvious. A single change can have broad impact across many use cases, and behavioral changes may only surface in specific contexts, making them harder to detect through casual testing.
Implementing Regression Testing
Creating regression test sets requires covering your critical functionality comprehensively. Include test cases for critical paths that represent essential user journeys, edge cases that probe boundary conditions and unusual inputs, and known issues that previously caused problems to verify they stay fixed. Ensure diverse scenarios spanning different categories and topics rather than clustering tests around a few patterns.
Comparing Test Runs
Tracking performance over time means comparing test runs systematically. Establish baselines by running comprehensive tests before making changes, capturing the expected behavior. Update baselines carefully, only after validating that improvements are real and intentional rather than updating every time results change. Use version control to track which baseline corresponds to which release, making it possible to understand historical performance. Document why baselines were updated so future decisions can consider the reasoning behind changes.
Best Practices
For test coverage, ensure your regression suite includes critical paths covering essential user journeys, edge cases with boundary conditions and unusual inputs, and tests for known issues that were previously fixed. Maintain diverse scenarios spanning different categories and topics to catch issues that only surface in specific contexts.
For baseline management, establish baselines by running comprehensive tests before changes to capture expected behavior. Update baselines carefully and only after validating that improvements are genuine and intentional. Track which baseline corresponds to which release through version control, and document why baselines were updated to inform future decisions.
For execution strategy, run regression tests pre-deployment before promoting changes to production. Implement regular schedules such as nightly or weekly execution to catch issues that emerge over time. Set up triggered runs that automatically execute on code changes, providing immediate feedback. Use a layered approach starting with quick smoke tests, then proceeding to the full regression suite only if smoke tests pass.
For handling non-determinism in AI systems, set thresholds that allow some variance in responses since identical inputs may produce different outputs. Run tests multiple times to account for randomness and look for consistent patterns rather than fixating on individual failures. Focus on trends rather than one-off anomalies. Implement human review for borderline failures to make final pass/fail determinations.
Regression Test Patterns
Maintaining a golden dataset provides a curated set of inputs with known expected behavior patterns. These high-quality examples represent important use cases and serve as a stable reference point. Continuous monitoring runs subsets of regression tests continuously in production, catching issues as they occur rather than only during deployment. Before-and-after comparison involves running identical tests before and after changes, making degradation immediately visible through direct comparison.