Pre-training vs Fine-Tuning in Large Language Models
Pre-training vs Fine-Tuning in Large Language Models
Pre-training is where the model learns general language patterns. Fine-tuning adapts it to specific tasks.
1) Pre-training
- Trained on massive internet-scale data
- Objective: predict next token
- Builds general intelligence patterns
2) Fine-Tuning
- Uses smaller domain-specific datasets
- Improves accuracy in niche areas
- Can be supervised or reinforcement-based
3) Enterprise Trade-Off
Fine-tuning improves specialization but increases infrastructure complexity. Many companies prefer RAG instead of heavy fine-tuning.
4) Summary
Pre-training builds general capability. Fine-tuning builds domain expertise.
