Module 13
Fine-Tuning & Custom Models
Build AI that thinks exactly like your domain requires
Lessons
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13.1
When to Fine-Tune vs. Prompt
Locked40 min
13.2
Dataset Engineering
Locked90 min
13.3
OpenAI Fine-Tuning Pipeline
Locked90 min
13.4
Open-Source Fine-Tuning
Locked120 min
13.5
Model Evaluation & Benchmarking
Locked75 min
13.6
Deploying Custom Models
Locked60 min
Expert Lens
Founder
Think like a founder: what user pain, distribution channel, pricing, and retention loop does this AI capability unlock?
Systems
Think like a first-principles systems builder: separate demos from durable products, and measure the bottleneck the model actually removes.
Infrastructure
Think like an AI infrastructure leader: account for data, latency, cost, reliability, evaluation, and deployment constraints before scaling.
Objectives
- Fine-tune LLMs for domain-specific tasks
- Create, evaluate, and improve custom models
- Deploy at production scale
- Build dataset pipelines that make fine-tuning work
Capstone
Domain Expert Model
Fine-tune an open-source model on a professional domain, evaluate against baseline, and deploy as a production API.
A running custom model outperforming the base model on your target task.
Real Examples
- A solo operator replaces repetitive research, drafting, and QA loops with an AI-assisted workflow while preserving human review.
- A startup validates an AI feature by measuring time saved, accuracy, user retention, and willingness to pay before building a platform.
- An enterprise team moves from chatbot demo to governed internal assistant with retrieval, permissions, observability, and escalation.
Mastery Checks
- Explain the core concept without buzzwords.
- Build a small working artifact, not just a note or prompt.
- Define an evaluation rubric and test against at least five edge cases.
- Estimate operational cost, failure modes, and human review requirements.