LLM Evaluation (Evals)
Definition
LLM evaluation ("evals") is the discipline of measuring whether an AI system actually works: is the answer correct, grounded, safe, and useful? Because LLM outputs are non-deterministic free text, traditional unit tests are not enough. Evals combine curated test datasets, automatic metrics, LLM-as-judge scoring, and human review to turn "it seems fine" into numbers you can track across model, prompt, and pipeline changes.
Why it matters for AI jobs
Every serious AI team treats evals as the backbone of shipping: you cannot upgrade a model or edit a prompt safely without a regression suite. Job descriptions increasingly say "experience with evaluation frameworks" explicitly. For a senior engineer, evals experience signals that you have run LLMs in production, not just demos — it is one of the clearest differentiators in AI hiring today.
Key concepts
- Golden dataset — a curated set of inputs with expected outputs or grading criteria.
- LLM-as-judge — using a strong model with a rubric to grade outputs at scale, validated against human labels.
- RAG metrics — retrieval recall/precision, faithfulness (groundedness), answer relevance.
- Agent metrics — task success rate, steps/cost per task, tool-call error rate.
- Regression testing — running the eval suite on every prompt/model/pipeline change, in CI.
- Observability & tracing — logging inputs, outputs, latency, and cost per request (e.g. Langfuse, Braintrust).
- A/B and online evaluation — user feedback signals and shadow deployments in production.
Learning path
- Take any LLM feature you have built and write 30 test cases with pass criteria in a spreadsheet or JSON file.
- Automate: script that runs the cases against your pipeline and reports pass rate; wire it into CI.
- Add LLM-as-judge with a clear rubric; spot-check judge decisions against your own labels to calibrate.
- Adopt an evals/observability tool (promptfoo, Langfuse, or Braintrust) and trace a real application.
- Practice the workflow: change a prompt or swap a model, and use the eval report to decide ship / no-ship.