How RAG Works: A Technical Guide for Developers
Retrieval Augmented Generation (RAG) is the architecture behind most production AI applications.
The Problem RAG Solves
LLMs have limitations:
- Knowledge cutoff: Training data ends at a point
- Hallucination: Models generate false information confidently
- No private data: Generic models don't know your content
RAG solves all three by grounding responses in retrieved documents.
High-Level Architecture
User Query → Embedding → Vector Search → Context Assembly → LLM → Response
↑
Document Store (your knowledge base)
Step-by-Step Process
Step 1: Document Ingestion
- Chunking: Split documents into pieces (200-1000 tokens)
- Embedding: Convert chunks to vectors
- Indexing: Store in vector database
Step 2: Query Processing
- Query embedding: Convert query to vector
- Similarity search: Find most similar chunks
- Retrieval: Pull top-k relevant chunks
Step 3: Context Assembly
Combine retrieved chunks with the query in a prompt.
Step 4: LLM Generation
The LLM generates a response grounded in provided context.
Key Technical Decisions
Chunking Strategy
- Fixed-size vs. semantic chunking
- Smaller = precise retrieval, less context
- Larger = more context, harder to retrieve
Embedding Models
- OpenAI text-embedding-3
- Cohere embed-v3
- Open-source: BGE, E5, GTE
Vector Databases
- Pinecone (managed)
- Weaviate (open-source)
- Qdrant (performance)
- pgvector (PostgreSQL)
Common Pitfalls
- Wrong chunk size - Experiment and measure
- Ignoring document structure - Preserve hierarchy
- No evaluation framework - Build test sets
RAG is straightforward in concept, complex in production.