AI Dev Skills
Model Training & Fine-tuning
What is it?
Adapting pre-trained models to specific domains, tasks, or behaviors using your own data. Fine-tuning can dramatically outperform prompt engineering on specialized tasks.
Why it matters for AI PMs
Generic models underperform on domain-specific tasks by 15-40% in most enterprise use cases. Fine-tuning on 1,000 domain examples often beats the best prompts on the largest models.
The 2026 landscape
Unsloth made fine-tuning accessible — 2x speed, 70% less memory. LoRA/QLoRA is the standard efficient method. GRPO (from DeepSeek) has replaced PPO as the preferred RL method.
What strong coverage looks like
4+ fine-tuning repos indicates a team that has moved beyond off-the-shelf models. They are customizing behavior, reducing hallucination on domain tasks, and building proprietary model capabilities.
Your library coverage (0 repos)
No repos in this skill area yet.
Key concepts to know
- •LoRA and QLoRA (parameter-efficient fine-tuning)
- •RLHF, DPO, and GRPO (alignment techniques)
- •Supervised fine-tuning (SFT) on instruction data
- •Synthetic data generation
- •Catastrophic forgetting prevention