Definition #
The process of adapting OpenAI’s GPT-4 model to improve performance on niche tasks (legal analysis, medical Q&A) by training it on specialized datasets.
Key Characteristics #
- Cost: $0.03/1k tokens (training)
- Methods: LoRA (Low-Rank Adaptation), RLHF
- Typical dataset size: 50-10k examples
- Output control: Temperature, top-p sampling
Why It Matters #
Fine-tuned GPT-4 models achieve 94% accuracy in domain tasks vs 68% for base models (Stanford HAI, 2023).
Common Use Cases #
- Custom enterprise chatbots
- Technical documentation generation
- Compliance report analysis
Examples #
- OpenAI Fine-Tuning API
- Azure OpenAI Service customization
- Third-party platforms: Together.ai, Modal Labs
FAQs #
Q: How much data is required?
A: Start with 500+ high-quality examples. More complex tasks need 10k+.
Q: Can I fine-tune GPT-4 cheaply?
A: Use parameter-efficient methods like LoRA to reduce costs by 80%.