A legal AI platform uses chain-of-thought reasoning to improve contract risk assessment accuracy. The system's CoT outputs are displayed to users (attorneys) as 'reasoning traces.' Attorneys report that Claude's visible reasoning sometimes leads to correct final answers via incorrect intermediate steps — for example, citing the wrong legal precedent in the reasoning but reaching the correct risk conclusion. The team debates whether to (A) hide the reasoning trace, (B) validate intermediate steps before displaying, or (C) add instructions for Claude to verify its own reasoning. Which approach is architecturally correct given that attorneys rely on reasoning traces to build trust?

Approach B implemented as two-stage pipeline with external validation is architecturally correct. The core problem is that intermediate reasoning steps cite legal precedents that should be verifiable — an incorrect precedent citation in the reasoning trace is a factual error, not just a model opinion. Breaking the pipeline into: (1) generate structured reasoning steps with explicit precedent citations, (2) validate each cited precedent against a legal database via tool calls, (3) generate final assessment using only validated reasoning — creates a reliable, auditable process. Attorneys can then trust the displayed reasoning because each step has been externally verified. Option A (hiding reasoning) destroys the trust-building purpose of the system — attorneys explicitly need to see reasoning to validate AI-assisted legal work; this is a UX and professional responsibility requirement. Option C (self-verification) has a well-documented weakness: a model that generates an incorrect intermediate step is likely to confirm it as correct during self-review — the same biases produce both the error and the validation. Option D (extended thinking) separates internal from displayed reasoning but doesn't fix the underlying factual accuracy of the displayed conclusion — attorneys still need to understand why the risk assessment was reached.