Systems, agents, retrieval, infrastructure

Fine-Tuning, Distillation, and Synthetic Data Pipelines

Fine-tuning is no longer the default answer to every AI quality problem, but it remains powerful when teams need style consistency, specialized classification, tool selection, or lower-cost task execution. This module teaches when to tune, when to retrieve, when to prompt, and when to distill.

Deep dive summary

What this module actually teaches.

Passage 1Fine-tuning is no longer the default answer to every AI quality problem, but it remains powerful when teams need style consistency, specialized classification, tool selection, or lower-cost task execution. This module teaches when to tune, when to retrieve, when to prompt, and when to distill.

Passage 2Learners examine dataset design, labeling quality, synthetic data generation, rejection sampling, eval leakage, model distillation, preference optimization, and deployment rollbacks. The curriculum highlights operational risks such as stale data, brittle benchmarks, and training a model to imitate mistakes in generated examples.

Passage 3The practical outcome is a tuning decision framework. Students learn to justify training work with measurable targets, build safe datasets, evaluate before and after behavior, and ship specialized models without breaking the surrounding AI runtime.

Learning operating system

Module prerequisites, concepts, outcomes, and artifacts

Prerequisites

  • Prompting and evals
  • basic supervised learning vocabulary
  • data quality concepts

Key concepts

  • fine-tuning decision criteria
  • synthetic data generation
  • distillation
  • preference optimization
  • eval leakage
  • dataset governance
  • model rollback

Target audience

  • ML-adjacent software engineers, AI platform teams, and product engineers evaluating whether custom models are worth the effort.

Outcomes

  • Choose between prompting, RAG, fine-tuning, and distillation
  • Design datasets that match measurable product outcomes
  • Evaluate custom model behavior without leaking test data into training

Artifacts

  • fine-tuning readiness checklist
  • synthetic data quality rubric
  • read-only dataset card example

Code example

Real patterns, ready to copy into your editor.

A code reference card that frames this module's concepts in real syntax. Copy any example to study it or adapt it directly in your own editor.

Copy-ready snippets
curriculum/se-finetuning-distillation-synthetic-data.tsts
1type ModuleBrief = {2  track: string;3  difficulty: string;4  keyConcepts: string[];5  reviewGate: 'human-review-required';6};7 8export const moduleBrief: ModuleBrief = {9  track: 'Software Engineers',10  difficulty: 'Advanced',11  keyConcepts: [12    'fine-tuning decision criteria',13    'synthetic data generation',14    'distillation',15    'preference optimization',16    'eval leakage',17  ],18  reviewGate: 'human-review-required',19};