RAG (Retrieval-Augmented Generation) Basics

RAG is a pattern used in artificial intelligence, where we first fetch relevant documents/snippets, then pass them to a language model for a grounded response. In other words, apart from the training data known to the model, the LLM will retrieve and use, internal company documents, as part of its context, before generating and answer.

The RAG Flow

1. Ingest: Convert sources (PDFs, pages, knowledge base) into embeddings or hybrid indexes.
2. Retrieve: Given a user query, fetch top-N relevant chunks.
3. Construct Prompt: Inject those chunks plus instructions.
4. Generate: Model produces an answer referencing the provided material.
5. Evaluate & Monitor: Check quality, citation coverage, safety.

Benefits

• Up-to-date information without retraining.
• Controllable scope (only approved docs).
• Better factual accuracy & traceability.

Common Enhancements

• Hybrid Search: Combine semantic + keyword filtering.
• Chunk Scoring: Re-rank by relevance or freshness metrics.
• Answer Verification: Post-check factual claims.

RAG helps you say: “Show me answers strictly from our corpus.” That’s powerful for internal or regulated domains.