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         │
│                                                                     │
└──────────────────────────────────────────────────────────────────