What is Fine-tuning?
Fine-tuning is retraining (a copy of) an LLM's weights on your examples to teach it style, format, or domain-specific behavior.
Definition
Fine-tuning is the process of taking a pre-trained LLM and continuing training on a smaller, focused dataset to change the model's behavior — typically tone, output format, or domain expertise. In 2026 the dominant techniques are LoRA (Low-Rank Adaptation) and full fine-tuning, applied to open models like Llama 3.3 8B / 70B, Mistral Small / Large, or Qwen. Fine-tuning is the right call when RAG plateaus on style/format/latency — not when you need to inject knowledge (RAG does that better). About 12% of Paisol's AI builds use fine-tuning; 85% use RAG only.
Related reading
Want to ship something with this technology?
Free 30-minute strategy call with a senior engineer. We'll quote your project in writing within 48 hours.
Book My Strategy Call