# Bedrock Haiku Finetune Checklist

> Checklist-style workflow for preparing prompt–completion training data and evaluating a fine-tuned Claude 3 Haiku model in Amazon Bedrock.

- Skill: `uygnoey/bedrock-haiku-finetune-checklist` (Agent Skill)
- Install (CLI): `npx skillmds@latest add uygnoey/bedrock-haiku-finetune-checklist`
- Raw SKILL.md: https://api.skillmd.com/api/skills/uygnoey/bedrock-haiku-finetune-checklist/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: uygnoey (https://skillmd.com/u/uygnoey)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/uygnoey/bedrock-haiku-finetune-checklist

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## Instructions

Use this skill as a lightweight project checklist when you are about to fine-tune Claude 3 Haiku in Amazon Bedrock.

1. Define the target task and success criteria
   - State the task in one sentence.
   - Decide how you will measure improvement (accuracy, consistency, formatting compliance, latency/cost).

2. Build prompt–completion pairs
   - Collect representative inputs.
   - Write the ideal completions you want the model to produce.
   - Keep formatting consistent with the desired production output.

3. Validate data quality
   - Remove ambiguous or contradictory examples.
   - Ensure labels and rubric match the completions.
   - Check for sensitive data and compliance requirements.

4. Run an initial fine-tune and evaluate
   - Test on a held-out evaluation set.
   - Compare against a baseline model.
   - Inspect failure cases and add targeted examples.

5. Iterate and prepare for deployment
   - Repeat fine-tuning with updated data until metrics and qualitative checks meet your bar.
   - Plan the deployment path in Bedrock (console or API) and monitor ongoing performance.

## Examples

- Classification moderation example: create labeled prompt–completion pairs for categories like insults, threats, or explicit content, then evaluate accuracy improvements against your baseline.
- Structured output example: build prompt–completion pairs where completions adhere to your report/schema format and verify formatting consistency during evaluation.