RAG Explained: How AI Systems Stop Hallucinating

RAG solves the single biggest problem with LLMs in production: they make stuff up.

The Core Problem Language models are trained on static data. Ask them about your company's Q3 policy update from last month — they don't know. Worse, they'll fabricate a plausible-sounding answer.

How RAG Works 1. **Index your knowledge base** — chunk documents, embed them as vectors, store in a vector DB (Pinecone, Weaviate, pgvector) 2. **Query time** — user asks a question, the query is embedded and semantically searched against your vector store 3. **Retrieve** — top-k relevant chunks are pulled 4. **Augment** — the retrieved context is injected into the LLM prompt 5. **Generate** — the LLM answers using the retrieved context, not its parametric memory

The Technical Stack (Common) ``` User Query → Embedding Model → Vector Search → Retrieved Docs ↓ LLM + Retrieved Context → Answer ```

RAG in Oracle HCM Context Several Oracle partners are building RAG systems on top of HCM documentation to power AI chatbots for HR queries. The architecture typically indexes: - Oracle Fusion documentation - Company HR policies - Past support tickets

When RAG Isn't Enough Complex multi-hop reasoning, structured data queries, and real-time data still need additional patterns (GraphRAG, SQL agents, tool calling).