Giving AI Access to Your Data

Retrieval-Augmented Generation (RAG) has become the standard approach for building AI applications that need access to private or current information. Here's how it works.

The RAG Pipeline

A typical RAG system includes:

• Document Processing: Chunk documents into manageable pieces

• Embedding: Convert text to vector representations

• Vector Database: Store and index embeddings (Pinecone, Weaviate, etc.)

• Retrieval: Find relevant chunks for each query

• Generation: LLM synthesizes answer from retrieved context

Why RAG Matters

RAG solves critical limitations of LLMs:

• Knowledge cutoff dates no longer matter

• Hallucinations reduced through grounding

• Citations possible for every claim

• Works with proprietary data

"RAG turned our internal documentation into a conversational interface. Support tickets dropped 60%." — Head of Engineering at enterprise company

Best Practices

Building effective RAG systems requires attention to:

• Chunk size optimization (typically 500-1000 tokens)

• Overlap between chunks for context preservation

• Hybrid search combining semantic and keyword matching

• Reranking retrieved results before generation

Advanced Techniques

Cutting-edge RAG includes query decomposition, multi-step retrieval, and agentic RAG where the system decides when and what to retrieve.