A financial analysis system uses Claude to evaluate investment proposals. Initial testing shows Claude produces confident-sounding conclusions that are sometimes poorly reasoned. An architect wants to improve reasoning quality without changing the model. Which prompting technique most reliably elicits higher-quality step-by-step reasoning?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you need a prompt that makes Claude lay out the reasoning before the conclusion. Enumerating factors, weighing evidence, and checking counterarguments forces a complete reasoning trace, so you catch weak reasoning before it becomes a confident answer.
Full Explanation
The structured reasoning trace is correct because explicit structural decomposition is the most effective chain-of-thought elicitation technique here. Requiring Claude to enumerate key factors, evaluate each factor with supporting evidence, identify risks and counterarguments, and then state a conclusion with a confidence level forces the model through a complete reasoning process before committing to a conclusion. This catches errors that surface during structured enumeration. The vague instruction to think carefully is wrong because it's too vague — it nudges the model toward slightly more deliberate generation but does not constrain the structure of reasoning or require evidence citation, leaving reasoning quality improvement to chance. Increasing temperature to 0.9 is wrong because higher temperature increases output variability and randomness, not reasoning depth — it makes the model more likely to explore unconventional ideas but also more likely to make unsupported leaps. Using few-shot examples that demonstrate correct answers without showing reasoning steps is wrong because it trains the model to pattern-match to conclusions, which is precisely the problem being observed — it does not improve the reasoning chain.