Train a Model, Build a Skill, or Set Up RAG? Here’s How to Actually Decide
I hit this exact fork in the road last week. I had a big pile of my own material sitting on my hard drive, and I wanted AI to actually USE it. So I asked myself the question everybody eventually asks: “Do I need to train my own model?”
Turns out, that’s usually the WRONG first question. And the reason why saved me a ton of time (and money). So let me walk you through how I think about it now. 🤖
The three roads you can take
When people want AI to work with their own stuff (documents, notes, courses, company data, whatever), there are basically three ways to do it. Here they are in plain English, no jargon:
1. Training (fine-tuning) a model
Rewiring the AI’s brain with thousands of examples so it permanently behaves a certain way by default.
2. Building a Skill
Handing the AI an instruction sheet plus a few reference files right before you ask your question.
3. Building a RAG system
Giving the AI a private search engine for your files, so it looks up the relevant bits before answering. (RAG just means “retrieval augmented generation,” which is a fancy way of saying “search first, then answer.”)
The one idea that makes this easy: facts vs. behavior
Here’s the thing that unlocked it for me. I asked a handful of the top AI models this same question, and they ALL landed on the same distinction:
Skills and RAG are about FACTS (what the AI knows).
Fine-tuning is about BEHAVIOR (how the AI acts).
Once that clicked, the whole decision got a LOT simpler. If you want the AI to know your stuff, that’s a facts problem (Skill or RAG). If you want the AI to consistently act or sound a certain way no matter what, that’s a behavior problem (fine-tuning).
Most people THINK they have a behavior problem. Almost always, they have a facts problem. ❗
When each one is the right call
| Approach | Pick it when… | Cost & upkeep |
|---|---|---|
| Skill | Your material is small and curated (a few docs, a style guide, an FAQ) and doesn’t change much. | Cheap. Fast. You update it by editing a file. |
| RAG | You’ve got a LOT of documents (or they change often), and you need answers pulled from specific sources. | Medium. Some setup, ongoing tuning, but it scales well. |
| Fine-tuning | You need a very specific behavior, tone, or output format repeated reliably every single time, and prompting alone can’t nail it. | Expensive. High effort, and you retrain every time your data changes. |
The mistakes people make (so you don’t have to)
- Fine-tuning: Trying to use it to teach the AI new FACTS. It doesn’t really work. The model still forgets and makes stuff up. Fine-tuning changes how it behaves, not what it knows.
- Skill: Cramming in too many files and hoping the AI finds the good parts. More context is NOT always better. Too much junk just confuses it (and slows it down and costs more).
- RAG: Building it before you actually need it. It’s easy to over-engineer a whole search system for a pile of documents that would’ve fit in a simple Skill.
The biggest myth
“You have to train your own model to make it sound like you (or know your business).”
Nope. A well-written Skill (a good style guide plus a few solid examples) gets you about 90% of the way there, for a tiny fraction of the cost and effort. Don’t get me wrong, fine-tuning has its place. But it’s rarely where you should START.
My rule of thumb: start small, escalate only when you have to
Here’s the exact order I’d tell a friend to follow:
- Start with a Skill. It’s the cheapest, fastest, easiest to change. It honestly solves most problems in an afternoon.
- Move to RAG when your documents outgrow what fits in a single prompt, or they change too often to keep pasting in by hand.
- Fine-tune LAST, and only when the AI already has the right facts but keeps getting the tone or format wrong in a way you can’t fix with better instructions.
Notice the pattern? You climb the ladder only when the rung you’re on actually breaks. That’s the whole trick. Most people jump straight to the hardest, most Expensive option because it SOUNDS impressive. Then they spend weeks on something a simple Skill would’ve handled in an afternoon. 🚀
The bottom line
One thing I’ve learned over and over with this stuff: the fanciest option is almost never the right first move. Try the simple thing. Measure how it does. Only reach for the heavy machinery when the simple thing genuinely can’t keep up.
Ask yourself the facts-vs-behavior question first. It’ll point you the right direction almost every time. Then start on the cheapest rung and climb only if you have to.
How about you? Have you been sitting on a pile of your own material, wondering how to get AI to actually use it? Start with a Skill this week. I think you’ll be surprised how far it gets you. 😊
P.S. The best part of starting small isn’t just saving money. It’s that you LEARN what you actually need before you commit to building it. Every experiment teaches you something. And that beats guessing every time.
