Technology
Fine-tuning
Fine-tuning is training a base LLM on your domain-specific data so it performs better on your tasks without long prompts. Use cases: highly-structured outputs, brand voice, domain-specific classification.
More detail
In 2026, fine-tuning is mostly unnecessary for SMBs — frontier models with RAG cover 95% of needs at lower TCO. Fine-tune only when (a) prompts are >2K tokens and you can't shorten them, (b) you need consistent brand-voice outputs in production, or (c) you're running at scale where per-token cost matters more than initial training cost.