RAG for Business: a Chatbot on Your Documents

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.

Applied to human resources, this principle gives an assistant that answers employees on leave, health cover or expense claims: our guide on answering employee HR questions with an AI assistant covers it tool by tool.

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

Let's talk about your project

Contact details