LLM reliability #2-3: output validation and building strong evals first
goyalshaliniuk · x · 2026-10-10
Parts 2-3 of goyalshaliniuk's LLM reliability series:
2. Validate every output — enforce structured outputs, validate JSON schemas, check required fields, apply business rules.
3. Build strong evaluation tests — representative datasets, edge cases, accuracy/relevance metrics, and comparisons across model and prompt changes. If you don't measure quality, you can't reliably improve it.
Related event: Seven Ways to Make LLMs More Reliable: From RAG to Production Monitoring(9 posts)→
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