Evaluate LLM Outputs Systematically

Stop eyeballing

To improve a prompt or model reliably, you need a repeatable score. Start with a small eval set of representative inputs and expected outcomes.

Three grading styles

• Exact/structured — for tasks with a right answer (classification, extraction).
• Heuristic — checks like "contains the citation" or "valid JSON."
• LLM-as-judge — a model grades responses against a rubric; great for open-ended text, but validate it against human ratings.

Close the loop

Run the eval on every prompt change. A 30–50 example set, version-controlled alongside your code, catches regressions long before users do and turns prompt-tuning into engineering rather than guesswork.