Fine-Tuning is the process of adapting a pre-trained foundational AI model to specific tasks or private datasets, improving its accuracy and specialized perform
Fine-tuning continues a pretrained model's training on your own examples, typically thousands of prompt/response pairs, so desired behavior moves into the weights. Most 2026 fine-tuning is parameter-efficient (LoRA/PEFT): instead of updating billions of parameters, small adapter matrices are trained, cutting cost dramatically while preserving base capabilities. The workflow is data curation → training runs → evaluation against a held-out set → deployment of the adapted model.
Fine-tuning buys consistency and efficiency that prompting can't always reach: stable style and format at scale, domain vocabulary, lower latency and cost (smaller tuned models replacing larger prompted ones), and on-prem deployment for data-sensitive industries. It's the standard play when prompt + RAG plateaus below the quality bar.
Fine-tune for behavior (style, format, domain reasoning) and efficiency at volume; use RAG for changing facts that need citations; use prompting first because it's cheapest to iterate. The professional move is benchmarking all three on your task before committing.
With LoRA, useful behavior shifts often start around a few hundred to a few thousand high-quality examples; quality and diversity beat raw volume. Poorly curated data degrades models faster than small data limits them.
Unreliably: weights are a poor database. Fine-tuning shapes behavior and style; for factual knowledge that must be current and verifiable, RAG remains the right mechanism.