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

1. **Chunking**: Split documents into pieces (200-1000 tokens)

2. **Embedding**: Convert chunks to vectors

3. **Indexing**: Store in vector database

Step 2: Query Processing

1. **Query embedding**: Convert query to vector

2. **Similarity search**: Find most similar chunks

3. **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

1. **Wrong chunk size** - Experiment and measure

2. **Ignoring document structure** - Preserve hierarchy

3. **No evaluation framework** - Build test sets

RAG is straightforward in concept, complex in production.

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