# What It Knows: Memory, Training, Knowledge

An AI employee seems to "just know" things, but its knowledge comes from a few very different places, and they are not equally reliable. Knowing which is which tells you when to trust an answer and how to make it better.

## Three kinds of knowing

There are three separate sources behind any answer. First is what it learned during training: a broad, general grasp of language and the world, frozen at the moment training ended. This is why it can write a decent email about almost anything, and also why it has no idea about your last meeting or anything recent. It is general, not personal, and it does not update on its own.

Second is memory: the specific things it has picked up about you and your business across your conversations, so it does not start from zero every time. Third is a knowledge base: documents you hand it directly, like a price list or a policy. Training is the background education, memory is what it remembers about working with you, and the knowledge base is the file cabinet you give it to look things up in.

- Training data: general knowledge, learned in advance, fixed, not about you.
- Memory: what it remembers about you and your work over time.
- Knowledge base: documents you give it to pull exact facts from.

## Teaching it your business

You do not retrain an AI employee to teach it your business; you connect your documents and let it look things up. When you ask a question, it first retrieves the relevant pieces from your files, then writes its answer using those pieces in front of it. The official name for this look-it-up-then-answer pattern is RAG, short for Retrieval-Augmented Generation.

The payoff is that answers come from your facts instead of a guess. Point it at your real pricing, contracts, or product docs and it stops speaking in generalities and starts quoting what is actually true for you. Better source documents make better answers, so feeding it clear, current files matters more than any clever wording in your request.

## Why it makes things up

Because the engine is built to produce fluent, plausible text, it would rather give you a confident answer than admit a gap. When it lacks a fact, it can fill the hole with something that sounds right but is simply wrong, stated with the same calm certainty as a correct answer. The technical word for this is hallucination, and the danger is exactly that it does not look like a mistake.

You cannot remove it entirely, but you can sharply reduce it. Ground the work in a knowledge base so it has real facts to pull from, ask it to cite where an answer came from, and treat anything important, like numbers, names, or dates, as something to verify rather than trust on sight.

- It happens most when a fact is missing and the answer sounds confident anyway.
- Grounding it in your documents gives it real material instead of guesswork.
- Asking for sources, and checking key facts, catches the rest.

## Key takeaways

- Answers come from three places: general training, memory of you, and documents you provide.
- Training is fixed and general; it does not know anything recent or specific to you on its own.
- RAG means it looks things up in your files first, so it answers from your facts, not a guess.
- A "hallucination" is a confident wrong answer that fills a gap in what it knows.
- Grounding it in good documents and asking for sources is how you keep answers honest.

## Continue learning

- [Working With AI Employees](/en/free-ai-courses/working-with-ai-employees) — View the complete course.
- [Next: What It Can Do: Tools, Integrations, MCP](/en/free-ai-courses/working-with-ai-employees/what-it-can-do)