Evaluation: Stop Vibing, Start Measuring

The single biggest reason AI features fail in production is the lack of evals. "Vibe checking" works for demos and dies in scale.

The eval stack

1. A golden dataset — 20-200 representative inputs with expected outputs.
2. A grader — code (regex / JSON match), another LLM, or a human.
3. A baseline — what's the score today on your current model?
4. A regression loop — re-run when you change the prompt or model.

Types of graders

• Exact match — for structured outputs (JSON, code).
• LLM-as-judge — a stronger model rates the response 1-5 with a rubric.
• Embedding similarity — for "is this semantically close enough?"
• Human — slow but the gold standard. Use sparingly to calibrate the others.

Custom benchmarks in LLMAtlas

Upload a CSV of {input, expected} pairs. Pick three candidate models. Run. Get a scored leaderboard. The whole loop takes a few minutes and gives you a defensible answer to "which model should we use?"