Fine-tuning
Training an existing AI model on your own data to improve performance on specific tasks or style. Less common for no-code agents, which typically use prompts and RAG; fine-tuning is more relevant for custom coding or highly specialized domains.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
A marketing agency fine-tunes a small model on 5,000 of their past blog posts to capture their distinctive editorial voice. The fine-tuned model drafts new posts that pass editorial review with minimal rewriting—saving 3-4 hours per post versus prompting a generic model.
Frequently asked questions
- Should I fine-tune or just use better prompts?
- Start with prompts and RAG—they're cheaper, faster to iterate, and easier to update. Move to fine-tuning only when: (1) prompting hits accuracy ceilings on a critical task, (2) the task pattern is stable enough that retraining isn't needed often, and (3) you have enough high-quality examples (typically 500+) for the fine-tune to be worthwhile. Most production AI agent deployments never need fine-tuning—prompts and retrieval handle 95% of cases.