# Fine-Tuning Guide — Agentic Design


## Overview

Plan, train, evaluate, and operate model adaptations with explicit trade-offs and safeguards.

## What Fine-Tuning Changes

Fine-tuning updates some or all of a pretrained model's parameters using task examples or preference feedback. It can improve measured behavior (output structure, tone, task accuracy) but does not automatically add fresh knowledge, eliminate hallucinations, or make a model safe.

## Good Reasons to Test Fine-Tuning

- A stable, repeated behavior resists prompt-only fixes
- You have representative examples and a trustworthy evaluation set
- Shorter prompts or a smaller model may improve serving economics
- The chosen checkpoint and artifacts can be deployed and governed

## Reasons to Use Another Approach

- Use retrieval or tools for changing or attributable knowledge
- Improve prompts when the desired behavior fits clear instructions
- Fix the product or data pipeline when the model lacks required context
- Delay training when data rights, quality, or evaluation are unresolved

## Release-Oriented Workflow

1. Write the product goal, acceptance rubric, and failure budget
2. Create a sealed holdout from production-shaped cases
3. Measure prompt-only, few-shot, retrieval, and tool-use baselines
4. Select a licensed base model and smallest practical adaptation method
5. Run a short, reproducible pilot and inspect learning curves
6. Compare quality, regressions, latency, throughput, and total serving cost
7. Canary the winning candidate with monitoring and tested rollback

## Minimum Comparison Report

- Base checkpoint, prompt, retrieval, and tuned candidate on same holdout
- Per-slice task results with sample counts
- Safety and base-capability regressions with release thresholds
- Training configuration, seed, code revision, data revision, artifact hashes
- Measured latency, throughput, memory, and cost on serving stack
- Named owner, canary plan, monitoring signals, rollback procedure