ky-thuat Intermediate
What is Fine-tuning?
The process of further training an existing AI model on your data so it gets better at a specific task or matches a specific style.
Updated: May 2, 2026 · 1 min read
Fine-tuning is the practice of taking a pre-trained AI model (like GPT, Llama, or Claude) and continuing to train it on your own data so it absorbs your style, domain knowledge, or output format.
Quick comparison: Fine-tuning vs RAG vs Prompting
| Factor | Prompting | RAG | Fine-tuning |
|---|---|---|---|
| Setup cost | Cheap | Medium | Expensive |
| Update data | Instant | Instant | Re-train |
| Best for | General tasks | Document-grounded Q&A | Consistent style/format |
When to use fine-tuning
- You need consistent brand voice across all output
- You need a specific output format (complex JSON schema)
- You have lots of high-quality domain examples that prompting + RAG can’t match
When NOT to use fine-tuning
- You’re getting started — try prompting and RAG first
- Your data changes constantly — RAG fits better
- Budget < $1000 and no ML engineer
Lightweight alternatives
- LoRA — fine-tune with 100-1000× less memory
- Prompt caching — feed long context cheaply if RAG is too complex
Related
Tags
#fine-tuning#training