RAG (Retrieval-Augmented Generation) – Designing a Reliable AI Assistant Connected to Internal Data

This training enables technical professionals to design a RAG system capable of querying an internal document corpus and producing contextualized, traceable, and more reliable responses than a standalone LLM. It combines architectural understanding, technological choices, practical implementation, and evaluation, with a logic directly applicable in business contexts: technical documentation, internal support, quality procedures, HR databases, or business reference frameworks. The approaches adopted align with current market best practices, where data quality, chunking, retrieval, and evaluation determine the real performance of a RAG project.

• Understand the principles, benefits, and limitations of a RAG architecture in an enterprise context.
• Design a complete pipeline for ingestion, vectorization, indexing, retrieval, and generation.
• Compare multiple model selection strategies based on cost, privacy, performance, and architectural constraints.
• Implement a vector database tailored to business needs and existing information systems.
• Optimize chunking, indexing, and search strategies to enhance answer relevance.
• Evaluate the quality of a RAG system using dedicated metrics, including faithfulness and relevancy.
• Define deployment conditions for a RAG system that is operational, maintainable, and governed in a professional environment.