RAG: How AI Enriches Its Responses with External Data

Large language models like ChatGPT are impressive, but they have a major limitation: they can invent facts. RAG, or Retrieval-Augmented Generation, solves this problem by combining information retrieval and text generation. This approach allows the AI to draw from external documents before formulating a response, making its outputs more accurate and up-to-date.

The LLM Hallucination Problem

Language models are trained on enormous amounts of data, but they don’t “know” everything. When a question involves recent or company-specific information, they can invent plausible but false details. For example, an HR chatbot might claim that a vacation policy has changed when it hasn’t. These “hallucinations” undermine user trust.

What is RAG exactly?

RAG adds a retrieval step before generation. Instead of answering directly, the system first retrieves relevant passages from a document base (PDF, web pages, knowledge bases). These extracts are then provided to the model as additional context. Result: the response relies on real facts rather than solely on the model's memory.

The Three Steps of RAG Operation

• Indexing: the documents are split into small chunks and transformed into numerical vectors (embeddings) stored in a vector database.
• Retrieval: when a question arrives, it is also transformed into a vector and compared to the documents to find the most similar passages.
• Generation: the selected passages are injected into the LLM prompt, which then produces a response grounded in this information.

A Concrete Example

Imagine a company that wants an internal assistant. Without RAG, the model responds to “What is the expense reimbursement procedure?” with general or invented information. With RAG, it first retrieves the latest PDF of the internal policy and precisely cites the authorized amounts and deadlines. The user gets a reliable and traceable response.

Advantages and Points of Attention

• Advantages: more precise responses, the ability to update knowledge without retraining the model, and traceability of sources.
• Points of attention: quality depends on the relevance of the indexed documents and the performance of the retrieval system. Poor text chunking can lead to incomplete responses.

from langchain.chains import RetrievalQA
qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())
print(qa.run("Quelle est la politique de télétravail ?"))

RAG transforms LLMs into truly useful tools for businesses and professional applications. By combining retrieval and generation, it offers an excellent balance between ease of implementation and response reliability. If you're just starting out, begin by indexing about ten documents and test the results: you'll quickly see the difference in quality.