Designing a Decomposition-Driven LLM Workflow for a High-Stakes Corporate Task
You are leading an internal enablement team building an LLM-powered assistant to produce a board-ready “AI Risk & Compliance Brief” from a 60-page draft policy, 12 incident tickets, and 3 regulator updates. The brief must be accurate, traceable to sources, and consistent across sections (e.g., the risk taxonomy used in the executive summary must match the detailed findings). You have observed two failure modes in a prototype: (1) the model generates a good outline but later sections contradict earlier claims, and (2) some sub-questions (e.g., “Which incidents map to which regulatory obligations?”) are still too complex and the model gives shallow answers.
Write an essay proposing a concrete end-to-end problem-decomposition workflow that addresses these failures. Your answer must: (a) show how you will generate sub-problems (including at least one example of a well-formed sub-problem), (b) explain how you will solve sub-problems sequentially while carrying forward context using explicit Q/A pairs (include a short illustrative snippet of what the prompt/context for a later step would look like), (c) identify at least one sub-problem that should be decomposed recursively and demonstrate what the next level of sub-problems would be, and (d) justify the tradeoffs of your design (e.g., quality/consistency vs. token cost/latency, and how your approach reduces contradictions and shallow reasoning).
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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You are building an internal LLM assistant to answ...
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