1Cademy - Choosing and Justifying a Prompting Strategy Under Context and Quality Constraints

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Essay

Choosing and Justifying a Prompting Strategy Under Context and Quality Constraints

You are deploying an LLM inside an internal finance tool that must answer multi-step policy questions (e.g., “Is this expense reimbursable given these 6 policy clauses and these 3 trip details?”). Constraints: (1) you have a strict context window budget, so you can include at most ONE short worked example in the prompt; (2) the model’s first-pass answers are often incomplete (it sometimes explains reasoning but forgets to state a final decision); (3) you need a workflow that is reliable across many different question shapes, not just one template.

Write an essay proposing a concrete prompting workflow that combines: in-context learning (if you use it), chain-of-thought prompting (including whether you would use zero-shot CoT), explicit problem decomposition (including whether you would use a least-to-most progression), and an iterative self-refinement loop. Your answer must (a) explain why your chosen combination fits the constraints, (b) describe the exact sequence of LLM calls or prompt sections you would use, (c) explain how intermediate sub-answers feed into later steps, and (d) specify how you will force a clear final decision despite the risk of incomplete zero-shot CoT outputs. Include at least two tradeoffs you are accepting (e.g., latency vs. accuracy, context budget vs. guidance, error propagation vs. transparency) and how you would mitigate them.

Updated 2026-02-06

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