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RAG Architecture

Retrieval-Augmented Generation: Grounding AI in your data.

LLMs hallucinate. RAG solves this by retrieving relevant facts from your trusted data source and passing them to the LLM to generate an answer.

Documents
Vector DB
LLM

The Pipeline

  1. Ingestion: Load PDFs, Docs, Databases.
  2. Chunking: Split text into manageable pieces (e.g., 500 characters).
  3. Embedding: Convert chunks into vectors (numbers).
  4. Retrieval: When a user asks a question, search for the most similar vectors.
  5. Generation: Send the user question + the retrieved chunks to the LLM.