llm-patternslisted

# LLM Patterns Patterns for building, evaluating, streaming, and testing LLM-powered applications. ## Evaluation Quality assessment and validation of LLM outputs using LLM-as-judge, multi-dimension scoring, pairwise comparison, hallucination detection, and batch evaluation. Integrates with Langfuse for score tracking. - Use a separate judge model (e.g., GPT-4o-mini, Claude Haiku) to avoid self-evaluation bias - Score across 3-5 dimensions: relevance, accuracy, completeness, coherence - Quality threshold: 0.7 for production, 0.6 for drafts **Full patterns and code:** See [references/evaluation.md](${CLAUDE_SKILL_DIR}/references/evaluation.md) ### Autonomous Evaluation Loop Use `/experiment` to autonomously iterate on evaluator quality (judge prompts, scoring rubrics) against golden datasets, tracking agreement via Langfuse Experiments API. See [references/autonomous-evaluation.md](${CLAUDE_SKILL_DIR}/references/autonomous-evaluation.md) for the full pattern and a worked example. --- ## Streaming Real-time token delivery via Server-Sent Events (SSE) for better UX. Covers OpenAI sync/async streaming, FastAPI SSE endpoints, TypeScript frontend consumers, streaming with tool calls, and backpressure handling. - Use SSE for web, WebSocket for bidirectional communication - Buffer 50-200 tokens; set 30-60s timeouts for long responses - Always close connections properly and handle stream errors **Full patterns and code:** See [references/streaming.md](${CLAUDE_SKILL_DIR}/re