Pattern preview · 12 of 4,089 sample rules shown · site-specific intelligence stays private
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llm_evaluation
ai-agents-llm-evaluation-need-to-automatically-assess-llm-outputs-for-hallu-7026e5c7
IFNeed to automatically assess LLM outputs for hallucinations, relevance, and safety.
THENLeverage Opik's Datasets and Experiments to run automated evaluations. Use built-in LLM-as-a-judge metrics (e.g., Hallucination, Moderation, Answer Relevance) or define custom metrics. Evaluations can be integrated into CI/CD with pytest.
Tier 170%
llm_evaluation
observability-llm-evaluation-you-need-to-evaluate-llm-application-outputs-using-0a493981
IFYou need to evaluate LLM application outputs using automated judges, user feedback, or custom pipelines.
THENIntegrate Langfuse evaluation system via API or SDK. Use LLM-as-a-judge, manual labeling, or custom evaluation runs.
Tier 170%
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What you see here is the public tier-1 slice. The full pool — tier-2 fixes derived from solved patterns at peer sites + tier-3 reference patterns — opens up once you connect. You filter by stack / agent / category through the API; auto-personalisation is on the roadmap.
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