Large Language Models (LLMs) are incredibly powerful, but their training is static and they lack access to internal private documents. Retrieval-Augmented Generation (RAG) solves this by querying dynamic documents at prompt time, eliminating hallucinations and ensuring real-time compliance.

How the RAG Pipeline Operates

When a user prompts a custom chatbot, the system does not send the query directly to the LLM. Instead, it: 1. Converts the prompt into a mathematical vector representation. 2. Performs a semantic search against a vector database (like Pinecone or pgvector) containing chunked company PDF manuals. 3. Appends the relevant text chunks to the model prompt as verified context. 4. The LLM generates a localized answer citing the retrieved sources.

RAG pipelines allow businesses to deploy intelligent internal assistants that browse standard operating procedures, contracts, and legacy records securely and accurately.