Artificial intelligence
Definition RAG – Retrieval-Augmented Generation
Publiée le September 2, 2025
Publiée le September 2, 2025
Generative artificial intelligence has become a must-have. Large language models(LLMs) such as GPT-4, LLaMA or Claude are capable of writing text, generating code and simulating conversations with impressive fluency. However, they suffer from a major limitation: their responses are based solely on the data used during their training, which quickly becomes obsolete. What’s more, they are prone to hallucinations, i.e. the production of information that is false but presented as true.
This is where the RAG (Retrieval-Augmented Generation) technique comes into its own. It combines the power of generative models with the precision of documentary research. Simply put, a RAG-based system doesn’t invent its answer: it fetches information from an external knowledge base, integrates it into its reasoning, and then produces a much more reliable textual output.
At Palmer Consulting, we see RAG as one of the pillars of the new generation of applications in generative AI, especially for companies that want to exploit their own data securely.
RAG is based on two complementary mechanisms: information retrieval and augmented generation.
When a user asks a question, the text is first transformed into digital vectors using embeddings. These vectors enable the user’s query to be compared with documents stored in a vector database. Well-known solutions include FAISS, Weaviate, Pinecone or Milvus. The system then selects the most relevant passages in relation to the question posed.
The retrieved documents are then injected into the language model prompt. Rather than relying solely on its internal memory, the LLM feeds on this external data and produces a contextualized response.
A concrete example: if a user asks “What is an airline’s refund policy in 2025?”, a conventional LLM is likely to give a generic or even false answer. With RAG, the model directly consults the airline’s updated documents and provides a precise, verifiable answer.
This approach integrates the best of both worlds: the linguistic power of an LLM and the reliability of a constantly updated document base. This is exactly the type of architecture we are implementing in our AI agent agent projects at Palmer Consulting.
RAG is not just a theoretical innovation: it is already used in many sectors.
Companies can create intelligent chatbots capable of responding to customers with information from their own databases (FAQs, general terms and conditions, product guides). Thanks to RAG, each response is aligned with the company’s official documents.
In the field of law or academic research, RAG-based wizards can interrogate vast corpora (case law, scientific articles) and generate reliable summaries based on explicit sources.
Healthcare professionals can use RAG to obtain answers based on medical publications or validated protocols. This reduces the risk of error associated with the hallucinations of a conventional model.
Educational platforms use RAG to create virtual tutors capable of explaining a concept based on the school’s official courses. The added value here is twofold: reliability and personalized learning.
More and more organizations are integrating GAN into their digital transformation. At Palmer Consulting, we support our customers in implementing solutions where language models interact with their internal documents: technical manuals, intranets, CRM databases. The result? Intelligent, secure use of data, with an immediate return in terms of productivity.
Greater reliability: answers are based on real documents.
Continuous updating: no need to wait for new model training.
Customization: the ability to integrate company-specific data.
Traceability: some implementations allow you to cite sources.
Cost optimization: no need to retrain a massive model, just add an external base.
Dependence on the quality of the document base: if the data is obsolete, so is the answer.
Latency: searching for information can slightly lengthen response times.
Technical complexity: implementation requires an adapted AI architecture architecture and specialized skills.
Consistency of answers: the model may sometimes prefer its internal knowledge to the documents provided.
RAG has a bright future ahead of it:
Multimodality: integration not only of text, but also of images, audio and video.
Better management of long context: ability to operate sequences of tens of thousands of tokens.
Automatic verification: built-in mechanisms to check consistency between documents and answers.
Autonomous agents: combine RAG with intelligent AI agents capable of planning actions and collaborating.
Regulation and governance: new standards of transparency and traceability should reinforce confidence in these systems.
RAG (Retrieval-Augmented Generation) represents a decisive advance in the field of generative artificial intelligence. By linking language models to up-to-date knowledge bases, it guarantees more reliable, more relevant answers tailored to users’ specific needs.
For companies, GAN is a strategic opportunity: it enables internal data to be transformed into real levers of operational efficiency. Thanks to the support of experts such as a Artificial Intelligence Consultantit becomes possible to design virtual assistants, chatbots, search tools and decision support systems perfectly aligned with business realities.
In short, RAG is not just one technique among many: it is an essential building block for the future of AI, where performance, security and contextualization combine to bring the machine closer to human intelligence.