How does RAG reduce hallucination?

By grounding the model's answer in retrieved, approved documents rather than its memory, and by enabling it to cite sources or say when information is unavailable.

Does RAG respect our access permissions?

It should. In enterprise deployments retrieval is restricted to documents the user is permitted to see, so answers never expose unauthorised content.

Foundations

Retrieval-Augmented Generation

Adil Mektoub

Published 13 July 2026Last reviewed 13 July 2026

Definition

Retrieval-Augmented Generation: is a technique that grounds a language model's output in retrieved documents, so answers reflect trusted, current and permissioned data rather than the model's memory alone.

Executive summary

RAG retrieves relevant passages from an approved knowledge source and gives them to the language model as context before it answers. This keeps responses grounded in real, current information and lets the system cite its sources.

For enterprises it is essential: it constrains answers to approved internal knowledge, reduces fabrication, and respects access permissions on the underlying documents.

Key takeaways

RAG grounds answers in retrieved, approved documents.
It reduces fabrication and enables source citation.
It respects document-level access permissions.
It is how Knowledge AI gives trustworthy internal answers.

Architecture

A RAG pipeline has a few stages:

1IndexingApproved documents are prepared and stored for retrieval.
2RetrievalRelevant passages are found for a given question.
3AugmentationThose passages are given to the Large Language Model as context.
4GenerationThe model answers grounded in the retrieved content, with sources.

Business example

Example implementation scenario

An employee asks about an internal policy. RAG retrieves the current policy document and the model answers based on it, linking the source.

If no relevant document exists, a well-designed system says so rather than inventing an answer.

FAQ

Frequently asked questions

How does RAG reduce hallucination?: By grounding the model's answer in retrieved, approved documents rather than its memory, and by enabling it to cite sources or say when information is unavailable.
Does RAG respect our access permissions?: It should. In enterprise deployments retrieval is restricted to documents the user is permitted to see, so answers never expose unauthorised content.

Related concepts

Large Language Modelis an AI model trained on large volumes of text to understand and generate language, providing the reasoning and language ability at the core of an AI agent.AI Agentis a software system that uses a language model to reason about a task, call tools and take actions to complete it, operating within defined permissions and oversight.Enterprise AI Securityis the discipline of protecting the data, identities and actions involved in AI systems — through least-privilege access, encryption, isolation, auditability and human approval.Agentic AIrefers to AI systems capable of planning, selecting tools, executing multi-step tasks and adapting their actions toward a defined objective, rather than only responding to a single prompt.

Related GFO frameworks

GFO Enterprise AI Security FrameworkSecurity Framework — is GFO's layered approach to securing agentic AI — covering identity, data protection, tool safety, approval control, auditability and deployment options.

Author

Adil Mektoub

Founder · Engineering & AI Infrastructure

France-based AI platform engineer. Age 36. E-mobility AI background; SAP and Vitol.

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