Evaluating and Redesigning a Decomposition Workflow Under Context and Cost Constraints
You are leading an internal enablement team building an LLM-powered assistant to answer employee questions about a 120-page, frequently updated Benefits & HR policy PDF. The assistant must answer multi-hop questions (e.g., “If I switch from full-time to part-time mid-year, how does that affect my HSA eligibility and the employer match, and what forms do I need?”). You have a strict constraint: each user question can trigger at most 6 LLM calls, and the total context window is limited, so you cannot paste the entire policy or all prior intermediate work into every call.
A junior engineer proposes this workflow:
- Generate a fixed list of 10 sub-questions up front for any user query.
- Solve them in order.
- For each step, only pass the immediately previous sub-question and answer (not the original user question or earlier Q/A pairs).
- If a sub-question is hard, “just ask the model to think harder,” without further decomposition.
Write an evaluation of this proposed workflow and then propose a revised workflow that would be more reliable under the constraints. Your answer must (a) explain how you would generate sub-problems so they are neither too generic nor too rigid, (b) describe when and how you would apply recursive decomposition to a sub-problem that is still too complex, and (c) specify exactly what contextual information you would carry forward between sequential steps (e.g., which Q/A pairs, what summaries, what citations) and why that choice improves correctness while respecting the context-window and 6-call limits. Conclude by describing one concrete failure mode your redesign prevents and the mechanism by which it prevents it.
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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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