What are the main use cases for RAG?

RAG's biggest use cases are grounding chatbots in your company's real data, powering internal knowledge search, helping coding assistants pull in the right documentation, speeding up legal and medical research, and personalizing recommendations with current facts instead of guesses. The common thread: RAG lets an AI look things up before it answers, instead of relying only on what it memorized during training.

Where does RAG show up in the real world?

The same pattern — retrieve first, then answer — powers a handful of very different products:

• Customer support chatbots: a support bot retrieves your actual return policy or troubleshooting steps before answering, instead of making something up.
• Internal knowledge assistants: employees ask a question in Slack or a search bar, and the AI digs through internal wikis, PDFs, and shared docs instead of pinging a coworker.
• Coding assistants: the assistant pulls in your actual codebase, library docs, or API references so it suggests code that matches your project, not a generic example from the internet.
• Legal and medical research: instead of trusting an AI's memory of case law or clinical studies, RAG pulls the actual documents so the answer is anchored to a real, checkable source.
• Personalized recommendations: a shopping or content assistant retrieves your order history, browsing behavior, or current inventory before suggesting what to buy or watch next.

What makes a task a good fit for RAG?

The tell is simple: any job where being right and current matters more than sounding smart. If the answer depends on facts that live in a specific document — a policy, a spec, a patient record, a price — and those facts change or are too niche for a model to have memorized, RAG earns its keep. Tasks that are purely creative or general-knowledge usually don't need it.

Why not just use a plain LLM?

A plain model can only draw on what it saw during training, which is frozen at a point in time and blurry on details it only glimpsed once. It will happily fill gaps with confident guesses. RAG swaps that guessing for a lookup: the model answers from documents it can actually see, so its output stays tied to your data rather than its memory.