Fine-tuning is getting more accessible (and actually works now).
Fine-tuning is practical now—test it for specialized needs.
What changed
• Fine-tuning interfaces simplified across providers
• Lower data requirements (100s instead of 1000s of examples)
• Better documentation and success stories
Who it affects
• Teams with domain-specific needs
• Companies wanting brand voice consistency
• Apps requiring specialized knowledge
What to do now
• Collect and label high-quality training examples
• Define clear success metrics before starting
• Test fine-tuned model against baseline systematically
• Start small and iterate based on results