Step 1: What Are AI Evals and Why They Matter

Evals are automated tests for LLM outputs. Unlike unit tests they deal with probabilistic, open-ended results — so you need a mix of exact-match checks, heuristics, and model-based judges to get reliable signal.

Step 2: Designing Test Cases

Good test cases start with real user inputs, not synthetic ones. Collect examples from logs or user feedback, attach expected outputs or rubrics, and aim for coverage across common paths and known failure modes.

Step 3: Deterministic vs. Model-Based Scoring

Use deterministic scorers (exact match, regex, JSON schema validation) where possible — they're fast and cheap. Reach for a model judge only when the output is genuinely open-ended and a rubric can't be expressed as a function.

Step 4: Building an Eval Runner

An eval runner loops over your dataset, calls the model, runs each scorer, and aggregates results. Start simple: a Python script that writes a JSON report. Add concurrency and caching once the dataset grows beyond ~100 examples.

Step 5: Interpreting Results and Iterating

A passing score is not the goal — understanding which failure modes remain is. Bucket failures by category, find the highest-impact cluster, and iterate on the prompt or retrieval step for that slice before re-running the full suite.

Step 6: Automating Evals in CI

Add your eval runner as a CI step that blocks merges when score drops below a threshold. Cache model responses by input hash so reruns are fast. Store historical results to track regressions across prompt versions.

Next Mini Course

Advanced Eval Patterns – LLM-as-Judge at Scale – Go beyond basic evals: build multi-turn conversation evals, implement LLM judges with calibrated scoring rubrics, and scale your pipeline to thousands of examples with async runners and result dashboards.

Further Reading

• RAGAS – Framework for RAG and LLM eval pipelines
• Braintrust – Eval platform with model-based scoring support
• OpenAI Evals – Reference eval harness and task library