Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.
View on GitHubmelodic-software/claude-code-plugins
systems-design
January 21, 2026
Select agents to install to:
npx add-skill https://github.com/melodic-software/claude-code-plugins/blob/main/plugins/systems-design/skills/rag-architecture/SKILL.md -a claude-code --skill rag-architectureInstallation paths:
.claude/skills/rag-architecture/# RAG Architecture ## When to Use This Skill Use this skill when: - Designing RAG pipelines for LLM applications - Choosing chunking and embedding strategies - Optimizing retrieval quality and relevance - Building knowledge-grounded AI systems - Implementing hybrid search (dense + sparse) - Designing multi-stage retrieval pipelines **Keywords:** RAG, retrieval-augmented generation, embeddings, chunking, vector search, semantic search, context window, grounding, knowledge base, hybrid search, reranking, BM25, dense retrieval ## RAG Architecture Overview ```text ┌─────────────────────────────────────────────────────────────────────┐ │ RAG Pipeline │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ │ │ Ingestion │ │ Indexing │ │ Vector Store │ │ │ │ Pipeline │───▶│ Pipeline │───▶│ (Embeddings) │ │ │ └──────────────┘ └──────────────┘ └──────────────────────┘ │ │ │ │ │ │ │ Documents Chunks + Indexed │ │ Embeddings Vectors │ │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │ │ │ Query │ │ Retrieval │ │ Context Assembly │ │ │ │ Processing │───▶│ Engine │───▶│ + Generation │ │ │ └──────────────┘ └──────────────┘ └──────────────────────┘ │ │ │ │ │ │ │ User Query Top-K Chunks LLM Response │ │ │ └──────────────────────────────────────────────────────────────────