The Hallucination Problem

AIs forget, invent, and ignore your private data. Ask an AI how many legs a duck has -- it might sometimes answer 4. It is in its DNA: a mix of statistical probabilities.

LLMs are trained on enormous text corpora, but they do not know your internal documents, your refund policy, or your domain knowledge base. And when they do not know, they make things up -- this is what we call a hallucination.

The Solution: RAG (Retrieval-Augmented Generation)

RAG is a mechanism that connects AI to your knowledge (documents, PDFs, wikis, images, videos...) by retrieving relevant information before answering.

The result: precise, up-to-date responses, grounded in your documents.

A Concrete Example

A freshly deployed AI does not know your refund policy. Why? Because it was not part of its training data.

So, what are the options?

• Retrain the model? Count on millions of euros and weeks of computation.
• Fine-tune it? Expensive, complex, and not 100% reliable.
• Use RAG? The system searches your documents, reformulates the question, then answers with facts.

Here is the concrete flow:

1. Question: "What is our refund policy?"
2. Retrieval: the system extracts the relevant section from the PDF or wiki
3. Reformulation: "Returns are possible within 30 days. Answer the question..."
4. Response: "Returns within 30 days."

No more hallucinations. Just facts.

Concrete Use Cases

• Internal chatbots: answering employee questions based on internal documentation.
• Real-time customer support: providing precise answers based on FAQs and product manuals.
• Legal or healthcare assistants: generating responses with precise citations from reference texts.
• Developer tools: connecting the AI to your technical wiki or codebase.

How Does It Work?

The RAG architecture relies on four main components:

Vector DB

A vector database stores your documents as vectors for fast similarity-based search. Rather than searching for exact keywords, it finds the passages closest in meaning.

Embeddings

Embeddings transform your texts into semantic representations -- numerical vectors that capture the meaning of words and sentences. Two sentences that say the same thing differently will have similar embeddings.

Retriever

The retriever identifies the most relevant documents or passages in the vector database based on the question asked. It is the component that bridges the user's question and your knowledge base.

Generator

The generator (the LLM) writes the final answer based solely on the retrieved documents. It no longer "guesses": it synthesizes information from your sources.

Going Further

Of course, this is a simplified version. The RAG field is evolving rapidly and includes many advanced techniques:

• Chunking: intelligently splitting documents into optimally sized pieces for search.
• Contextual search: search techniques that take into account the overall context of the question.
• GraphRAG: using semantic links between documents to enrich retrieval.
• Clustering: thematic grouping of documents to improve relevance.
• HyDE (Hypothetical Document Embeddings): generating hypotheses to improve search.

Digitization with Meaning

At mAIstrow, we call this approach NeS: Numerisation en Sens (Digitization with Meaning). The idea is not just to digitize documents, but to give them meaning that AI can exploit.

Trust, precision, context: this is what your users expect. It is exactly what RAG unlocks.

Summary

RAG is the pragmatic answer to the hallucination problem in LLMs. Rather than retraining expensive models or settling for approximate answers, it anchors AI in your actual data.

It is an essential building block for any AI that aims to be reliable, precise, and useful in a professional context.