Part 8/12:
A meticulous data curation process was undertaken, emphasizing prompt development—utilizing tabular and descriptive prompts to communicate with LLMs. This approach mimicked natural human interactions, allowing models to better understand and interpret structured and unstructured data.
Crucially, instead of relying on retrieval-augmented generation (RAG) systems, the team opted for domain-specific supervised fine-tuning, which proved more suitable for holistic understanding rather than search-specific tasks.