Prompting, RAG, or fine-tuning? Choosing the right approach
By TechlyUpUpdated 2 min readDevelopers and product teams
Quick answer
Start with prompting — it's fastest and often enough. Add RAG when the model needs knowledge it doesn't have, such as your documents or recent data. Consider fine-tuning when you need consistent behaviour, format, or style that prompting can't achieve reliably, and you have quality training examples. Many systems combine approaches; the evaluation set decides.
Diagnose the problem
Different failures need different fixes.
- Wrong or missing facts about your data → retrieval (RAG).
- Right knowledge, wrong format or tone → better prompts, then fine-tuning.
- Task too complex → break into steps or use a stronger model.
- Inconsistent outputs → clearer instructions, examples, structured output.
Prompting first
Clear instructions, examples, and structured output solve many problems cheaply and can be changed in minutes.
When RAG fits
Knowledge that changes, is private, or is too large for a prompt belongs in retrieval, with sources you can cite.
When fine-tuning fits
Fine-tuning suits stable, well-defined behaviour with many good examples: a consistent classification scheme, a specialised format, or a particular style. It doesn't reliably add facts and requires ongoing maintenance.
Decision mistakes
Teams often choose the heavier option too early.
- Fine-tuning to add knowledge that changes frequently.
- Building RAG when the problem is unclear instructions.
- Skipping evaluation, so you can't tell which approach helped.
- Underestimating the maintenance cost of fine-tuned models.
Worked example: a classification feature
A team classifies support tickets into 20 categories. Prompting with descriptions and examples reaches decent accuracy, but confusion between similar categories remains. Adding retrieval doesn't help — the issue isn't missing knowledge.
With a few thousand labelled historical tickets, fine-tuning a smaller model improves consistency and reduces cost. The evaluation set made the decision clear at each step.
Try it yourself
List the top three failures in your AI feature and map each to prompting, RAG, or fine-tuning using the diagnosis list.
Frequently asked questions
Does fine-tuning teach the model new facts?
Not reliably. Use retrieval for knowledge; fine-tuning is better for behaviour and format.
How much data do I need to fine-tune?
It varies by task and provider. Quality and consistency of examples matter more than volume.
Can I combine RAG and fine-tuning?
Yes — for example, fine-tuning for output format and RAG for knowledge.
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Sources and further reading
- Hugging Face LLM Course
- Google Cloud: What is retrieval-augmented generation?
- OpenAI: Prompt engineering guide
Examples are authored practice material, not measured learner outcomes. Tool behavior can change. Found an error? Contact TechlyUp with the page URL and correction.