AI Engineering Mastery
Adapt behavior with training data

Fine-Tuning

Fine-tuning changes a model with task-specific examples. It is useful when prompting is not enough and the target behavior is stable and measurable.

Fine-tuning workflow
Base model
Curated examples
Training updates
Validation checks
Adapted model
Professional outcome

What you should be able to do

Know when to fine-tune, how to prepare data, how to evaluate results, and when RAG or prompting is the better tool.

CapstonePrepare a fine-tuning dataset and evaluation plan for a domain-specific assistant.
Essentials

Concepts to master

  • Supervised fine-tuning
  • Preference and reinforcement fine-tuning
  • Dataset formatting
  • Evaluation sets
  • Regression risk and model drift
Builder path

How to turn this topic into a working project.

Use this as the bridge from reading to implementation. The goal is to build a small, inspectable version before adding frameworks or production complexity.

  1. Collect a small set of high-quality examples that represent the stable behavior you want.
  2. Create train, validation, and test splits, then check for duplicates and sensitive information.
  3. Run a baseline with prompting or RAG first so fine-tuning has a real comparison point.
  4. Fine-tune only after the evaluation set can catch regressions in correctness, style, safety, and cost.
Primary sources

Start from authoritative material.

OpenAI supervised fine-tuning guideOpenAI fine-tuning platform updateOpenAI fine-tuning API referenceHugging Face PEFT documentationHugging Face TRL supervised fine-tuning documentationHugging Face TRL GitHub repositoryAxolotl GitHub repositoryLLaMA Factory GitHub repository
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