Fine-Tuning

Fine-tuning is the process of further training a pre-trained model on a smaller, domain-specific dataset so its behavior shifts toward a particular task or style, such as parsing resumes or drafting job descriptions in a consistent format. Unlike Retrieval-Augmented Generation (RAG), which fetches fresh content for each question, fine-tuning changes the model itself and is done ahead of use.

The two techniques solve different problems. Fine-tuning suits stable behaviors: output format, tone, task performance on a well-defined job. RAG suits facts that change and must stay current and private: policies, balances, org structure. The misconception is that fine-tuning teaches the model your policies. It does not do so reliably, and it cannot keep up with change; grounding does that. Darwinbox fine-tunes open-source models on anonymized HR datasets using parameter-efficient techniques and uses RAG for tenant-specific facts.