Building Your LLM Eval Framework and Running It in CI/CD

Most AI teams delay evaluation because a perfect framework feels out of reach. But the best eval framework is the small one you actually run. Start with a handful of golden examples and a few metrics, wire it into CI/CD, and grow it from production signals - not from a spec that never ships.

Start smaller than you think

You don't need a thousand cases on day one. A small, curated dataset of the inputs you care about most beats a large, muddy one. Aim for a set you can maintain and trust - even 30–50 high-signal examples is enough to catch regressions a human would notice. Coverage grows over time as you promote real failures from production.

The minimal metric set

Teams over-metric and never calibrate. A pragmatic starting point is a small, readable set:

• Two core quality checks - e.g., relevance and faithfulness, scored by a calibrated LLM judge.
• Three guardrail checks - safety, toxicity, and format or JSON-schema validity.

That "2 + 3" pattern covers quality and safety without drowning you in scores you don't trust. Add metrics only when you can explain what action a change in the number triggers.

Wire evals into CI/CD

An eval that runs in a notebook isn't a gate - it's a screenshot. The real framework runs on every pull request and blocks the merge when quality regresses:

# Every PR triggers the same evaluation

# 1. Load the golden dataset from the repo

# 2. Score the candidate run

# 3. Compare to the current baseline

# 4. Fail the build if quality or safety regresses

Because Infere stores evaluators in Git alongside your prompts, the same evals that gate a PR also give you a reproducible history. You can see exactly when quality changed and which change caused it - no more wondering whether that prompt tweak helped or hurt.

Grow coverage from production

The dataset is a living thing. Route failures from live traffic back into your golden set, and use monitoring to spot drift that your offline set didn't cover. Each release now proves two things: quality didn't regress, and you covered the failures users actually hit. That's the difference between a demo and a framework.

An evaluation you run every deploy beats a perfect one you run never.

Next steps

Build a small golden dataset, adopt a minimal metric set, and gate your pull requests on it. Pair running evaluation in CI/CD with production-grade LLM engineering and monitoring to close the loop between offline quality and live health. For the full evaluation taxonomy, see our LLM evaluation guide.