Building Custom LLM Chatbots Using RAG Architecture

The Power of Retrieval-Augmented Generation

Generic LLMs lack access to private enterprise data. RAG solves this by querying secure vector databases to inject relevant context before prompting the LLM.

How RAG Works

1. Document Chunking: Split internal manuals, policy PDFs, or database records into manageable text blocks.
2. Embedding Generation: Convert text chunks into numerical vectors and store them in databases like Pinecone or pgvector.
3. Context Injection: When a user asks a question, retrieve the most similar vectors, feed them to the LLM, and generate an accurate response.

RAG prevents AI hallucinations and keeps company data secure.