llm-app-patterns
Production-ready patterns for RAG pipelines, agent architectures, prompt management, and LLMOps monitoring.
- Covers five core RAG strategies: document chunking, embedding selection, retrieval methods (semantic, hybrid, multi-query, compression), and context-aware generation with citations
- Includes four agent patterns: ReAct (reasoning + acting), function calling, plan-and-execute, and multi-agent collaboration with specialized roles
- Provides prompt engineering practices: templating with variables, versioning and A/B testing, and prompt chaining for sequential workflows
- Details observability setup: metrics to track (latency, quality, cost, reliability), distributed tracing, and evaluation frameworks for response quality
- Includes production patterns for caching, rate limiting with exponential backoff retries, and multi-model fallback strategies
🤖 LLM Application Patterns
Production-ready patterns for building LLM applications, inspired by Dify and industry best practices.
When to Use This Skill
Use this skill when:
- Designing LLM-powered applications
- Implementing RAG (Retrieval-Augmented Generation)
- Building AI agents with tools
- Setting up LLMOps monitoring
- Choosing between agent architectures
1. RAG Pipeline Architecture
Overview
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