The moment you try to build an AI feature that actually knows about your business — your documents, your products, your policies — you hit two terms: RAG and fine-tuning. Most explanations are written for engineers. Here's the founder's version, and how to choose.
The two approaches, in plain English
A general AI model (like the ones behind ChatGPT) knows a lot about the world, but nothing about your business. There are two ways to fix that:
- RAG (Retrieval-Augmented Generation): You keep your business knowledge in a searchable store. When someone asks a question, the system retrieves the relevant bits and hands them to the model to answer with. Think of it as giving the AI an open-book exam with your documents.
- Fine-tuning: You retrain a model on your own data so the knowledge (or style) is baked in. Think of it as sending the AI on a training course.
The trade-offs that matter
Keeping knowledge current. RAG wins easily — update a document and the AI uses the new version instantly. Fine-tuning bakes knowledge in, so updating means retraining.
Cost and effort. RAG is usually cheaper and faster to build and run for most business use cases. Fine-tuning is more involved and more expensive to do well.
Accuracy on facts. RAG can cite the actual source, which reduces the AI making things up — a big deal if wrong answers cost you.
Tone and behaviour. Fine-tuning is stronger when you need the model to consistently behave or write a certain way, rather than just know certain facts.
Which one fits your use case
- "Answer questions from our docs / knowledge base / product catalogue." → RAG, almost always.
- "Support bot that knows our policies." → RAG.
- "We need the model to write in a very specific style/format every time." → Fine-tuning (sometimes both).
- "We have masses of proprietary data and specialised behaviour." → Fine-tuning, possibly with RAG on top.
For the large majority of small-business AI features, RAG is the right starting point — cheaper, current, and safer on facts. Fine-tuning is the specialist tool for when behaviour, not knowledge, is the problem.
Before you build either
The most important question isn't RAG or fine-tuning — it's whether an AI feature is the right thing at all. I wrote about that in build vs buy: when NOT to use AI. Get that right first.
Thinking about adding an AI feature to your product? That's core application development work. Book a free call and I'll give you an honest steer on the right approach — and whether it's worth doing at all.