RAG: plugging AI into your company's knowledge

RAG (Retrieval-Augmented Generation) is the architecture that turns a generic LLM into an expert on YOUR company: the AI draws its answers from your documents — procedures, contracts, project histories — instead of its training knowledge alone. It is today's most deployed enterprise architecture, and the most misunderstood.

The principle: for each question, the system retrieves the most relevant passages from your document base, then feeds them to the model as context to generate a sourced answer. Decisive advantages: answers cite their sources, the knowledge base updates without retraining any model, and your documents are never used to train anything.

RAG quality is 80% decided in document preparation. Obsolete or contradictory documents, multiple versions, poor chunking: all causes of wrong answers delivered with confidence. Auditing and governing the document corpus is the real first project — well before model selection.

The highest-ROI use cases we observe: internal support (HR, IT, legal) where teams answer the same questions in loops; technical pre-sales where product knowledge is scattered; compliance where finding THE right clause across hundreds of documents costs expert hours.

A well-scoped RAG pilot deploys in 6 to 10 weeks: a restricted, cleaned corpus, a pilot user group, systematic answer-accuracy measurement. Industrialisation follows with corpus widening, access-rights management and continuous quality monitoring.