Fine-Tuning
The process of taking a pre-trained AI model and further training it on a smaller, domain-specific dataset to improve performance on particular tasks.
What is Fine-Tuning?
Fine-tuning is a transfer learning technique where a pre-trained foundation model (like GPT-4, Llama, or Mistral) is retrained on a curated dataset specific to a particular domain, task, or style. The model retains its broad language understanding while learning specialised behaviour—like writing in a company's tone of voice, extracting fields from legal contracts, or classifying support tickets.
Fine-Tuning vs. Prompting vs. RAG
| Approach | Best For | Trade-off |
|---|---|---|
| Prompt engineering | Quick experiments, general tasks | Limited by context window |
| RAG | Knowledge-grounded Q&A | Retrieval quality is the ceiling |
| Fine-tuning | Style, format, domain expertise | Requires quality training data |
In practice, the most capable enterprise systems combine all three: a fine-tuned model with RAG retrieval, guided by well-engineered prompts.
Key Techniques
- Full fine-tuning — Update all model weights (expensive, powerful)
- LoRA / QLoRA — Low-rank adaptation that trains a small number of additional parameters (efficient, popular)
- Knowledge distillation — Train a smaller model to replicate a larger model's behaviour
- RLHF / DPO — Align model outputs with human preferences
The Blue Note Logic Perspective
Our Model Tuning & Distillation service helps clients decide when fine-tuning is genuinely needed (it often isn't—RAG + good prompts solve 80% of use cases) and execute it properly when it is. We specialise in LoRA-based fine-tuning on domain corpora, with rigorous eval pipelines that compare fine-tuned performance against baseline prompting. The biggest mistake we see: fine-tuning on too little data, which leads to catastrophic overfitting.