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ai-fine-tuning

Fine-tune models on labeled datasets using BootstrapFinetune workflows.

11|1|Updated Feb 8, 2026
Search Tags:#DSPy#machine-learning#finetuning#bootstrapfinetune#model-distillation#better-together
One-click install
npx skills add https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-fine-tuning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-fine-tuning
Source: https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills/tree/main/skills/ai-fine-tuning
Command: npx skills add https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-fine-tuning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fine-tuning models on your data to maximize quality and cut costs when prompts reach a ceiling, you have 500+ labeled examples, or you need domain-specific performance.

Core Features & Use Cases

  • Determine when fine-tuning is warranted versus prompt optimization.
  • Support BootstrapFinetune, model distillation, and BetterTogether workflows for end-to-end quality improvements.
  • Guidance on evaluation, deployment, and guardrails to maintain reliability.

Quick Start

Prepare a labeled dataset and run the BootstrapFinetune workflow to produce a fine-tuned, cheaper model.

Frequently Asked Questions about ai-fine-tuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
When should I fine-tune a model instead of optimizing prompts?▼

Fine-tuning is warranted when prompt optimization has plateaued, you have 500 or more labeled examples, or you need domain-specific performance with cheaper inference costs.

How do I fine-tune a model using BootstrapFinetune?▼

To fine-tune a model with BootstrapFinetune, prepare a labeled dataset and run the workflow to produce a fine-tuned, cheaper model tailored to your specific domain requirements.

What is the BetterTogether workflow for model fine-tuning?▼

BetterTogether is a fine-tuning workflow that combines with model distillation and BootstrapFinetune to provide end-to-end quality improvements for domain-specific model performance.

Can I use model distillation to cut inference costs?▼

Yes, model distillation is supported as a fine-tuning technique to maximize quality and cut costs by transferring knowledge from larger models to cheaper, domain-specific ones.

Do I need 500 labeled examples to start fine-tuning?▼

Having 500 or more labeled examples is a key indicator that you are ready for fine-tuning. If you lack this data, you should continue prompt optimization until your dataset grows.

How do I maintain reliability after fine-tuning a model?▼

Maintain reliability after fine-tuning by applying the provided guidance on evaluation, deployment, and guardrails to ensure the fine-tuned model performs safely in production.

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