Now What?

This talk addresses the challenge of applying large language models (LLMs) to domains where structured training data is scarce. The core thesis is that by providing LLMs with explicit rules, heuristics, and guidelines, similar to how junior professionals are mentored, their powerful reasoning capabilities can be effectively leveraged even in low-knowledge areas. This approach aims to bridge the gap between domains with abundant data and those lacking it, enabling LLMs to assist in complex problem-solving across a wider range of professions.