Guide · 36 pages
Vectorial AI in learning: beyond the chatbot.
Semantic search, RAG, embeddings — these concepts fundamentally change what corporate learning can do. This guide explains the how and the why, jargon-free.
01
Vectorization: from data to meaning
What is an embedding, how an LLM turns your knowledge base into semantic representations. Explained without math, with concrete learning examples.
02
RAG vs chatbot: real differences
Why a 'raw' chatbot hallucinates, and how Retrieval-Augmented Generation makes answers traceable, citable, and scoped to your context — essential in the enterprise.
03
7 concrete use cases
Learner copilot, path generation, HR internal search, capsule from docs, qualitative grading, drop-off detection, continuous knowledge indexing.
04
Pitfalls and best practices
How to avoid hallucinations, handle multilingual content, protect sensitive data, measure the quality of a RAG system in production.
Autres ressources