LLM Finetuning on custom documents for specialized responses
I contributed to a dataset creation effort for fine-tuning large language models (LLMs) by producing high-quality instruction–response pairs tailored to a specific enterprise domain. The scope involved identifying relevant operational scenarios, drafting clear end-user instructions, generating correct and domain-aligned responses, and formatting outputs into a structured JSON format suitable for supervised fine-tuning. The data labeling task focused on instruction engineering, response authoring, and quality control to ensure factual correctness, consistency in style, and alignment with the intended model behavior. Internal proprietary guidelines were followed for tone, format, and reasoning depth. Quality was ensured through multi-pass review, rejection of ambiguous prompts, normalization of labels.