---
title: "RAG that doesn't hallucinate: grounding your data"
description: "Retrieval-augmented generation is the most reliable way to put an LLM to work on your data, if you get the retrieval right. A guide to the parts that matter."
canonical_url: "https://www.tarmac.io/resources/rag-that-doesnt-hallucinate/"
last_updated: "2026-10-07"
---

[Resources](https://www.tarmac.io/resources/) / AI & Data

# RAG that doesn't hallucinate: grounding your data

Retrieval-augmented generation is the most reliable way to put an LLM to work on your data, if you get the retrieval right. A guide to the parts that matter.

Tarmac · June 24, 2026 AI & Data

Retrieval-augmented generation (RAG) is the workhorse of applied AI: instead of hoping a model memorized your domain, you retrieve the relevant facts at query time and ask the model to answer _from those facts_. Done well, it slashes hallucination and keeps answers current. Done carelessly, it just launders bad retrieval into confident prose.

## Retrieval is the hard part, not generation

Most RAG failures are retrieval failures. The model answered faithfully, from the wrong context. Where teams win or lose:

-   **Chunking.** Too big and you bury the signal; too small and you lose it. Chunk on semantic boundaries, not arbitrary character counts.
-   **Embeddings and search.** Pure vector search misses exact terms; keyword search misses meaning. Hybrid retrieval plus a reranking step consistently beats either alone.
-   **Freshness.** Your knowledge base changes. If your index doesn’t, your answers rot.
-   **Metadata and filtering.** Scoping retrieval by tenant, date, or permission is often the difference between “helpful” and “data leak.”

## Grounding and citations

Ask the model to answer only from retrieved context, and to cite it. Citations aren’t decoration. They make the system auditable and give users a way to verify. When the context doesn’t contain the answer, the correct output is “I don’t know,” not a plausible guess.

## Measure it like a search system

Evaluate retrieval quality (did we fetch the right passages?) separately from generation quality (did we use them faithfully?). You need both to improve either. A small, curated eval set catches regressions that a demo never will.

RAG isn’t magic. It’s a well-built search system with a language model on top. The teams that treat it that way are the ones whose AI features hold up.

Keep reading

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