The Discipline of LLM Evaluation
If you can't measure your LLM application's quality, you can't improve it. Building evaluation into the core of your development loop.
The teams shipping reliable LLM applications all share one habit: they measure quality obsessively. Without evaluation, every prompt tweak is a gamble and every model upgrade is a leap of faith.
Build an evaluation set first
Before optimising anything, assemble a representative set of inputs with known good outputs or clear criteria. This set is the ground truth against which every change is judged. It grows over time as you discover edge cases and failures.
Automate the judgement
Manual evaluation doesn't scale and doesn't run in CI. Automated metrics — exact match where applicable, model-graded rubrics for open-ended responses, faithfulness checks for grounded answers — let you evaluate hundreds of cases on every change.
- Curate a representative evaluation set early
- Automate scoring so it runs in CI
- Add every production failure to the set
- Track quality as a trend, not a snapshot
An LLM application without an evaluation set is a system you're flying blind.
Make it a gate
The final step is wiring evaluation into your pipeline as a gate. A change that regresses quality below threshold shouldn't ship. When evaluation is automatic and blocking, quality stops being a hope and becomes a guarantee.
Keep reading
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RAG That Actually Works: Beyond the Naive Pipeline
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