RAG (Retrieval-Augmented Generation) Basics

RAG combines retrieval and generation so answers stay grounded in real, approved content instead of guesswork.

2025-11-08•6 min readragretrieval

RAG (Retrieval-Augmented Generation) Basics

RAG is a pattern where we first fetch relevant documents/snippets, then pass them to a language model for a grounded response. Instead of hallucinating, the model is steered by real context.

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 + 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.