Reasoning trace extractor prompt
By default most models give you a conclusion with no visible path to it. When the answer is wrong, you are left guessing whether the model misread the task, made a bad assumption, or fumbled a calculation. A reasoning trace fixes this: you instruct the model to write out its working in a clearly marked block before committing to a final answer. This tool wraps your existing prompt with those instructions so the logic is exposed, checkable, and easy to parse.
How it works
The tool keeps your task prompt intact and prepends a short instruction layer. That layer tells the model to first produce a reasoning block — in the tag style you chose — and only then produce the final answer in a separate, clean section. The depth setting controls how much the model elaborates: brief asks for a few decisive steps, while thorough asks it to surface assumptions, consider edge cases, and explain each inference. Because the output structure is fixed, you can reliably split the response into “reasoning” and “answer” downstream.
What each tag style produces
XML-style tags are the most robust for programmatic parsing. The output looks like:
<reasoning>
The problem asks for X. First I need to consider Y, because...
Checking the edge case where Z...
Therefore the answer is...
</reasoning>
<answer>
[Final answer here]
</answer>You can extract the reasoning with a simple split or regex on tag boundaries. This is the recommended style for any application that logs or processes model output.
Markdown headings produce a more readable format for human review:
## Reasoning
Step 1: ...
Step 2: ...
## Answer
[Final answer here]Labeled prefix is a fallback for models that resist explicit tags but tend to follow Reasoning: / Answer: style labels.
When a visible trace is most valuable
- Arithmetic and logic tasks: Models often miscalculate silently. Seeing the intermediate steps lets you catch where a number went wrong.
- Classification or decision tasks: Tracing which criteria the model applied tells you whether it weighted factors the way you intended.
- Debugging stubborn failures: If a model consistently produces wrong answers on a class of inputs, the trace usually reveals a shared faulty assumption faster than prompt engineering by trial and error.
- Audit and compliance contexts: A visible reasoning trace provides a human-readable explanation alongside automated decisions, which is useful when decisions must be explainable.
The trace is not a faithful readout of internal computation
This is the critical caveat: a model’s written reasoning is its account of the logic, not a literal record of internal computations. A model can produce a coherent, step-by-step justification that is post-hoc rationalisation rather than the actual path taken. This is well-documented in NLP research. The trace is still far more useful than an unexplained answer — it gives you something to verify and correct — but do not assume that checking the trace guarantees the answer is right.
Hiding reasoning from end users
Because the trace is in a consistently tagged or labeled block, you can parse and strip it before showing output to users. Log the reasoning to your own database for review and show users only the clean answer section. This gives you auditability without exposing the model’s internal deliberation (which can look strange or overly hedged) in your user interface.