Designing Trust: UX Trade-offs in an LLM-Powered Contract Assistant

AI · 5 min read

ContractMate shipped a pilot that auto-summarized contract clauses using an LLM. Early testers loved the speed but were unsettled by occasional hallucinations. Designers faced a classic trade-off: raw model outputs offered convenience, while strict guardrails reduced immediacy and required more user work.

The team introduced layered explanations: a short, model-generated summary paired with a provenance bar that displayed source clause excerpts and confidence indicators. They also added conservative defaults for high-risk clauses, routing uncertain items to a human reviewer and surfacing clear rationale for why the assistant flagged content.

After these changes, user satisfaction rose by 18% and escape rates to manual review decreased, indicating increased trust without sacrificing the assistant’s speed. The case shows that UX patterns — provenance, confidence scores, and conservative escalation — are critical controls when LLMs are handling sensitive workflows.