Does RAG reduce AI hallucinations?

Yes, significantly, provided it is well designed. By forcing the AI to rely on retrieved passages and to cite its sources, RAG limits made-up answers. Best practices: careful chunking of documents, a good vector search engine, and a strict instruction to "answer only from the sources, otherwise say you don't know".

Which documents can be connected to a RAG system?

Almost any: PDFs, Word documents, Notion or Confluence pages, emails, support tickets, product sheets, contracts, internal procedures. You can also plug in a database. The challenge is not the format but the preparation: cleaning, chunking and indexing the content correctly so that search surfaces the right passages.

Is RAG GDPR-compliant?

It can be, and that is one of its strengths: your documents stay in your environment, the AI is not retrained on them, and you control who can access what. Best practices: European hosting of the vector database, fine-grained access rights management, logging and minimization. Essential in legal, healthcare and finance.

How much does an enterprise RAG project cost?

A scoped RAG assistant (one document base, a question-and-answer interface with citations) generally starts between €6,000 and €20,000 for setup, plus API costs and a maintenance plan. The cost depends on the volume of documents, the number of sources and the security requirements. The ROI comes from the time saved searching for information.

RAG for business: a chatbot on your document base (2026)

Q: Why use RAG rather than a model on its own?

A model on its own answers from its general knowledge and can make things up (hallucinate) about your internal topics. RAG grounds every answer in your real documents: procedures, contracts, product sheets, history. You get reliable answers about your context, updated as soon as your documents change, without retraining a model.

Definition

What is RAG?

RAG (retrieval-augmented generation) connects an AI model to your document base: instead of answering from memory, the AI first searches for the relevant passages in your documents, then writes an answer grounded in those sources. The result: up-to-date, company-specific and verifiable answers, with citations of the documents used.

In practice, your documents are turned into a « vector database »: each passage is indexed to be found by meaning, not just by keywords. When a user asks a question, the system retrieves the closest passages and provides them to the model as context. The AI no longer guesses — it synthesizes your sources.

The challenge

Why RAG rather than a model on its own?

A model on its own answers from general knowledge and can hallucinate about your internal topics. RAG grounds every answer in your real documents — procedures, contracts, product sheets, history. You get reliable answers about your context, updated as soon as your documents change, without retraining a model.

Criterion	Model alone	RAG
Source of answers	Model memory	Your documents
Freshness	Frozen at training	Continuously up to date
Hallucination risk	High on your topics	Strongly reduced
Citations / traceability	No	Yes
Updates	Retraining	Adding documents

Model alone vs RAG — Lumyniq, 2026.

How it works

How a RAG system works

Ingestion — collecting and cleaning your documents (PDF, Notion, emails, database).
Chunking & indexing — segmenting into passages, building a vector database.
Retrieval — for each question, searching for the most relevant passages.
Generation — the model (Claude, GPT, Mistral) writes an answer grounded in those passages, with citations.

This pipeline plugs naturally into Claude, connects to your tools via the MCP and is orchestrated with n8n.

Use cases

What RAG is used for in business

An internal assistant that answers team questions about procedures and documentation.
Customer support grounded in your knowledge base, with sources.
Legal or medical research across a corpus of case files, with traceability.
Sales enablement: instant answers on product sheets and pricing.

It's a key building block of our custom AI agents.

FAQ

Frequently asked questions about RAG

RAG (retrieval-augmented generation) is a technique that connects an AI model to your document base: instead of answering from memory, the AI first searches for the relevant passages in your documents, then writes an answer grounded in those sources. The result: up-to-date, company-specific and verifiable answers, with citations of the documents used.

RAG for business: a reliable chatbot on your document base

What is RAG?

Why RAG rather than a model on its own?

How a RAG system works

What RAG is used for in business

Frequently asked questions about RAG

Sources

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