Language models are very capable, but they have two uncomfortable limits: they don’t know your private data and they can “hallucinate” (confidently make up answers). RAG (Retrieval-Augmented Generation) is the technique that solves both, connecting the model to your own knowledge base.

What is RAG?

RAG combines two pieces: a retrieval system that searches your documents for relevant information, and a generative model that writes the answer using that information. Instead of answering only from what it “remembers” from training, the model answers grounded in data you provide in the moment.

The result: up-to-date answers, based on your private information, with fewer hallucinations and — ideally — with cited sources.

Components: how the pipeline works

RAG has two phases. First the knowledge is prepared (once), then it’s queried (on each question).

1. Ingestion / indexing (preparation)

  • Chunking: documents are split into manageable pieces.
  • Embeddings: a model converts each chunk into a numeric vector that captures its meaning.
  • Vector database: those vectors are stored in a store optimized for similarity search.

2. Retrieval and generation (on each query)

  • Semantic search: the user’s question is turned into a vector and the most similar chunks are found (the most relevant top-k). This is often combined with keyword search (hybrid search) and a re-ranking step to refine.
  • Augmentation: the retrieved chunks are injected into the prompt as context.
  • Generation: the model writes the answer leaning on that context.

What is it used for?

  • Assistants over your documentation: answering questions from internal manuals, policies or contracts.
  • Customer support: chatbots that answer with the company’s real, up-to-date information.
  • Smart search: finding answers, not just documents.
  • Reducing hallucinations: by anchoring the model to verifiable sources.

Best practices and limits

RAG is only as good as the data it retrieves: if the knowledge is messy or outdated, the answers will be too. The quality of chunking, embeddings and re-ranking determines the outcome. And while RAG reduces hallucinations, it doesn’t eliminate them entirely: human oversight is still needed for important decisions.

Keep reading: What is MCP? · MCP vs RAG.

At Grupo TANDEM we help companies set up AI assistants grounded in their own knowledge, securely and maintainably. If you want your AI to answer with your data, let’s talk.