An LLM application doesn’t throw an exception when it gets quieter, less accurate, or less helpful — it just does, silently, and the first signal is often a user complaint rather than a test failure. Continuous evaluation closes that gap.
Traditional software has a relatively stable relationship between code changes and behavior —…
A curated, versioned test set of representative queries with known-good expected outputs or scoring…
The evaluation pipeline’s infrastructure is usually the easier part; the harder, ongoing work…
Traditional software has a relatively stable relationship between code changes and behavior — a bug introduces a visible failure. LLM application quality depends on the model version, the prompt, the retrieval pipeline, and the underlying data, any of which can shift behavior in subtle ways that don’t throw errors. A model provider’s routine update, a seemingly minor prompt tweak, or a change to the document index can each quietly degrade output quality without any system treating it as a failure.
The evaluation pipeline’s infrastructure is usually the easier part; the harder, ongoing work is building and maintaining a test set that actually represents real usage and known failure modes. Production traffic (logged, reviewed, and curated with privacy safeguards) and past incident cases are the best sources — a test set written purely from imagined scenarios tends to miss the actual edge cases real users hit.
Continuous evaluation works best as a genuine CI/CD gate — integrated into the same pipeline that deploys prompt, model, or retrieval changes — rather than a separate, manually-triggered process that’s easy to skip under deadline pressure. Teams that have already adopted this discipline for hallucination testing specifically find it a natural extension to apply the same pipeline to broader quality dimensions: tone, completeness, instruction-following, and task success rate.
Usefulness depends more on representativeness than raw size — a few hundred well-chosen cases covering real usage patterns and known edge cases catch far more regressions than a much larger but narrow or synthetic set. Start smaller and grow it from real production incidents rather than trying to build an exhaustive set upfront.
It’s useful for subjective quality dimensions that are hard to score with exact-match methods, but it has its own failure modes and biases and shouldn’t be the only signal for high-stakes evaluations. Combining it with exact/semantic match methods and periodic human spot-review gives a more reliable overall picture.
On every change that could plausibly affect output quality — prompt edits, model version changes, retrieval pipeline or data updates — rather than on a fixed schedule, so a regression is always caught at the point of introduction.