Evaluation & Observability

Teams without evals tune their LLM systems by vibes: someone tries a prompt change, pastes three good outputs in Slack, and it ships. Then a regression surfaces in production a week later. The fix is not more caution — it is measurement.

What we build

• Eval datasets from real traffic — curated from your production traces and labeled failures, not synthetic questions. Fifty well-chosen examples catch more regressions than five hundred generated ones.
• Retrieval metrics separated from generation metrics — recall and ranking quality for the retriever, faithfulness and correctness for the generator, so a failure points at the layer that caused it.
• LLM-as-judge with calibration — automated grading where it works, spot-checked against human labels so you know how far to trust it.
• Regression harnesses in CI — prompt and pipeline changes gated on eval results, with per-example diffs so a drop is debuggable instead of just alarming.
• Tracing and observability — LangSmith (or equivalent) instrumentation so every production answer can be replayed: what was retrieved, what the model saw, what it returned, what it cost.
• Guardrails from failure data — validation and fallback patterns targeting failures you have actually logged, not a generic checklist.

Why this pays for itself

An eval suite changes the team's velocity: prompt changes stop being risky, model upgrades become an afternoon of running the suite instead of a week of manual QA, and disagreements about "which version is better" get settled by numbers.

This is also the engagement that makes every other engagement cheaper — hardening and retrieval work go faster when improvement is measurable.