"Should we use RAG or fine-tuning?" is one of the first questions we get when a team wants to add AI to their product. It's also one of the most misunderstood. The honest answer: for most products, you want retrieval-augmented generation (RAG), sometimes you want both, and you rarely want fine-tuning on its own. Here's how to tell which case you're in.
What RAG actually is
RAG keeps the model fixed and feeds it the right information at question time. You store your content — docs, policies, product data — as embeddings in a vector database, retrieve the most relevant pieces for each query, and ask the model to answer from that retrieved context.
You reach for RAG when the problem is about knowledge:
- The answers live in your data, not the model's training set.
- That data changes (prices, policies, inventory, new documents).
- You need answers grounded in sources you can cite or check.
Crucially, RAG fixes hallucination in a way a bigger model can't, because the model is answering from retrieved facts rather than memory.
What fine-tuning actually is
Fine-tuning changes the model itself by training it further on your examples. It's about behaviour, not knowledge:
- A consistent format, tone, or structure you can't reliably get from prompting.
- A narrow, repetitive task where you have many good input/output examples.
- Latency or cost pressure where a smaller fine-tuned model can replace a large general one at scale.
What fine-tuning does not do well is teach the model new facts reliably. Train a model on your latest pricing and it'll still happily invent a number next week. Facts belong in retrieval; fine-tuning shapes how the model behaves.
The rule of thumb
A simple way to decide:
- "The model needs to know something" → RAG.
- "The model needs to behave a certain way" → fine-tuning.
Most real products are knowledge problems wearing a behaviour costume. A support assistant, an internal copilot, a chatbot grounded in your docs — those are RAG, every time.
When you need both
The sophisticated answer is that they're complementary. A mature system often uses RAG for the facts and light fine-tuning (or, increasingly, just strong prompting and structured outputs) for the format and tone. You ground the what with retrieval and shape the how with fine-tuning. But you almost always start with RAG and add fine-tuning only if prompting can't get you the behaviour you need.
What most teams get wrong
The common mistake is reaching for fine-tuning first because it sounds more "real." It's usually the slower, more expensive path to a worse outcome: you spend weeks curating training data, you still can't update facts without retraining, and you've solved a knowledge problem with a behaviour tool.
Start with RAG. Add structured outputs and evaluation. Only fine-tune when you've proven that prompting and retrieval genuinely can't deliver the behaviour you need — and you have the data to support it.
That's the same order of operations we use when we build generative AI systems and integrate AI into products. If you're weighing the two for something specific, book a call and we'll give you a straight recommendation for your use case — not a one-size-fits-all answer.
