LLM reliability #3-4: strong eval suites and graceful abstention
goyalshaliniuk · x · 2026-10-10
Parts 3-4 of goyalshaliniuk's LLM reliability series:
3. Build strong evaluation tests — test against realistic examples before deployment: representative datasets, edge cases, accuracy/relevance metrics, comparisons across model and prompt changes. You can't improve what you don't measure.
4. Handle uncertainty gracefully — ask clarifying questions, say when information is missing, abstain when evidence is insufficient, escalate high-impact decisions.
Related event: Seven Ways to Make LLMs More Reliable: From RAG to Production Monitoring(9 posts)→
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