RAG: how Retrieval Augmented Generation transforms enterprise AI

RAG connects an LLM to your internal data for precise, sourced answers. Complete guide for SMEs. The problem: brilliant but ignorant LLMs: LLMs are trained on public data frozen in time. They don't know your products, internal procedures, contracts or technical documentation. Result: they hallucinate on company-specific questions. RAG solves this by giving the LLM access to your data at query time. How does RAG work?: RAG works in 3 steps: 1) Indexing: documents are chunked, converted to vector embeddings and stored in a vector database (Qdrant, Weaviate, pgvector). 2) Retrieval: relevant chunks are retrieved by semantic similarity. 3) Generation: the LLM generates its answer based exclusively on retrieved chunks, with cited sources. Enterprise use cases: Document assistant: a chatbot answering employee questions citing internal procedures. Intelligent knowledge base: semantic search in technical documentation. Augmented customer support. Contract analysis: extracting key information from hundred-page contracts. RAG pitfalls: Source data quality: garbage in, garbage out. Chunking strategy: too fine loses context, too broad drowns relevant information. Embedding model choice strongly impacts retrieval quality. Evaluation requires specific metrics (faithfulness, relevance, answer correctness). Deploying sovereign RAG with Powehi: Our sovereign RAG stack: Mistral for generation, French embedding model, pgvector on sovereign PostgreSQL, and custom UI. Everything hosted in France, no data leaves your environment.