RAG — what it is and why your business needs it
Retrieval-Augmented Generation lets AI answer based on your documents. No hallucinations, with source citations.
RAG = AI + your knowledge base. The model doesn't make things up — it cites specific fragments of your documents, offers, policies and instructions.
How it works: 1) split docs into chunks, 2) convert to vectors (embeddings), 3) store in a vector DB, 4) retrieve closest chunks for a query, 5) LLM answers based on them with citations.
Use cases: internal HR chatbot, support backed by docs, legal assistant, sales knowledge base, product FAQ.
Stack: OpenAI / Cohere embeddings + Pinecone / Supabase pgvector / Qdrant + LangChain / LlamaIndex / custom.
Benefit: employees stop hunting SharePoint for 20 minutes — they ask AI and get an answer with a source link in 3 seconds.
