"Chatbot," "RAG," and "AI agent" get thrown around as if they're the same thing. They're not, and picking the wrong one wastes money. Here's the difference in plain terms.
Chatbot
A conversational interface. Old chatbots followed scripted rules; modern ones use an LLM to sound natural. On its own, a chatbot only knows what the model was trained on — it can't see your prices, your docs, or your orders.
Use it when: you want a friendly front door for simple, general questions.
RAG (retrieval-augmented generation)
RAG gives the model your information. It retrieves relevant snippets from your documents, products, or knowledge base and feeds them to the LLM before it answers — so responses are grounded in your actual content, not guesses.
Use it when: you need accurate answers about your business — support docs, product details, policies.
AI agent
An agent doesn't just answer — it acts. It can call tools, take multiple steps, and complete a task: look something up, update a record, book a slot, send an email. Good agents combine RAG (to know things) with tools (to do things).
Use it when: you want work done, not just questions answered — qualifying a lead, resolving a ticket end to end, processing a form.
The simple test
Ask what you want the outcome to be:
- Just talk naturally → chatbot
- Answer accurately from our content → RAG
- Actually complete a task → agent
Most useful business tools end up being an agent with RAG inside it. If you're not sure which you need, that's a five-minute conversation — and a much cheaper one than building the wrong thing.