Kituwa IT · Field guide
RAG vs fine-tuning vs better prompting
Three paths, different jobs. Better prompting changes the instructions: role, constraints, examples, output format. Practical checks and official references.
Three paths, different jobs.
This guide expands the Facebook post into a practical workflow for beginners, home-lab users, and small-business owners who want to understand the trade-offs before changing a working system.
What this guide covers
- RAG vs fine-tuning vs better prompting
- Three paths, different jobs.
- Prompting — Instructions + format
- RAG — Retrieve current docs
- Fine-tuning — Teach style/pattern
- Review — Check the answer
Why it matters
The goal is not to memorize a product name or copy a command blindly. It is to understand what the change affects, verify it against an official reference, test it on spare hardware or a virtual machine when possible, and keep a rollback path.
A practical walkthrough
Step 1: what to check
Better prompting changes the instructions: role, constraints, examples, output format. It is the cheapest first step.
Check the assumptions
RAG retrieves reference material and puts it in the prompt. It is useful when answers depend on documents that change. Retrieval does not guarantee truth; the model can still misread or ignore the source.
Use the supported update path
RAG also needs a source of truth and a way to update or remove stale documents; otherwise retrieval can surface outdated policy. Fine-tuning changes model behavior through training data. It can teach style or a narrow pattern, but it is not a database and can become stale. It also needs data, evaluation, and maintenance. Fine-tuning needs an evaluation set so you can detect regressions.
Step 4: what to check
A fictional company handbook: prompting can set the format; RAG can fetch the current policy; fine-tuning can teach the tone. Start with the simplest path that solves the problem.
Your turn
Which path matches your changing data?
What to verify
- Use the official documentation for the exact version you run.
- Test on a spare device or virtual machine before changing a working system.
- Keep a backup and a rollback path.
- Record what changed and how you verified it.
Common mistakes
- Trusting a confident answer without a check.
- Skipping a repeatable test set or factual review.
- Sending private data to an endpoint you have not verified.
Quick checklist
- ☐ Three paths, different jobs.
- ☐ Prompting — Instructions + format
- ☐ RAG — Retrieve current docs
- ☐ Fine-tuning — Teach style/pattern
- ☐ Review — Check the answer
FAQ
What is the first step?
Three paths, different jobs.
What should I avoid?
Trusting a confident answer without a check.
Where can I verify the current details?
Use the official references below; versions and interfaces change.
References
Related guides
- Smaller model, better workflow?
- A reusable prompt that produces useful drafts
- The AI task you should keep reviewing
Need help?
If you want a second opinion on the setup or the rollback plan, Kituwa can review it with you. Start with the checklist above; it makes the conversation more useful.