What is RAG?
Retrieval-Augmented Generation (RAG) is an AI architecture that combines a large language model (LLM) with a document retrieval system. Instead of relying solely on the LLM's training data, RAG searches your company's internal knowledge base — policies, SOPs, product manuals, databases — and uses the retrieved context to generate accurate, source-cited answers.
Why RAG Over Fine-Tuning?
Fine-tuning an LLM on your company data is expensive, slow, and quickly becomes outdated. RAG is real-time — update a document in your knowledge base, and the AI immediately reflects the change. No retraining required.
Building a RAG Pipeline
- Document Ingestion: Load PDFs, Word docs, Confluence pages, database records, and email archives into a vector database (Pinecone, Weaviate, or pgvector).
- Chunking Strategy: Split documents into 500-1000 token chunks with overlap to maintain context across boundaries.
- Embedding: Convert chunks into vector embeddings using OpenAI Ada-002, Cohere, or open-source models like BGE.
- Retrieval: When a user asks a question, embed the query, find the top-K most similar document chunks, and inject them into the LLM prompt.
- Generation: The LLM generates an answer grounded in the retrieved context, with citations back to source documents.
Enterprise Use Cases
HR departments use RAG to answer employee policy questions. Support teams query product documentation instantly. Legal teams search contract clauses across thousands of agreements. The possibilities are transformative.