What is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) is defined as an AI architectural pattern that improves Large Language Model (LLM) responses by retrieving relevant information from private databases before generating an answer.
Detailed Architectural Context
Retrieval-Augmented Generation (RAG) bridges the gap between public AI knowledge and a company's private files. When a user asks a question, the RAG system converts the query into a vector representation, searches a vector database (like Pinecone) for matching text inside company manuals, PDFs, or contracts, and attaches those relevant chunks to the prompt sent to the LLM. This guarantees that the AI base its answers on actual company documentation, preventing hallucinations and preserving data privacy.
Related Technical Terms
LLM (Large Language Model)
A deep learning algorithm trained on massive text datasets that can summarize, translate, predict, and generate text, code, or structured JSON data.
API Gateway
A server that acts as an entry point for APIs, routing requests, enforcing rate limits, managing security, and aggregating data from backend microservices.
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