Sometimes you need a model that does one thing exceptionally well. Examples:
- Classification: Categorize support tickets, detect sentiment
- Extraction: Pull entities from documents, parse invoices
- Code generation: Match your codebase's patterns, prefer your frameworks
You can see this in action at companies like Databricks. They fine-tuned Llama on internal code and significantly outperformed off-the-shelf models. Task-specific fine-tuning beats general models when you have good training data for that exact task.