What It Is
A model such as an LLM is trained on a smaller dataset so it performs better in a particular domain, style, or format, rather than learning from scratch. The model adapts what it already knows, which is faster and cheaper than building a new model.
Key Points
- Typical uses: company terminology, ticket classification, and consistent output structure.
- Data needs: high-quality, well-labeled examples.
- Parameter-efficient methods: LoRA updates only a small number of added weights.
- Alternatives: prompt design and RAG are often cheaper and less risky.
Why It Matters
Fine-tuning is not always the right answer, and poor evaluation can cause overfitting or weaken general abilities. Clear success metrics, held-out test data, and ongoing monitoring show whether the tuned model truly improves on the original.
How ClearLeaff Applies It
We help enterprises decide when fine-tuning is worthwhile, then manage the full lifecycle of data preparation, training, evaluation, deployment, and monitoring through LLMOps.