人工智能助手

Its abilities depend on available context, tools, permissions, and product design. A conversational interface does not imply unlimited knowledge or access.

主要要点

• Explain the evidence behind an answer.
• Distinguish temporary context from persistent memory.
• Design for correction, cancellation, and handoff.

Clarify the task and available evidence. An assistant answering from a supplied document has a different basis from one answering from model knowledge or a live tool. It should make that distinction understandable when it affects reliability. Keep conversation state, saved memory, retrieved records, and training separate. A service remembering a preference does not necessarily mean that the underlying model was retrained. Users need clear controls over persistent information and connected accounts. Design interactions around progress and recoverability. Show whether an operation is proposed, running, completed, or waiting for information. Allow cancellation and correction without forcing a user to restart a long conversation. Confirm consequential details at a meaningful review point. Evaluate assistance through task outcomes and user effort. A long answer may create more work if it hides the next step or invents details. Test clarification behavior, unsupported questions, tool failures, and transitions between conversation and actual actions. Preserve a route to a person or a manual workflow when needed.

1. Imagine a user asking for a saved report. The assistant has drafted the text but has not written a file.
2. Label the result as a draft and show the save operation when available. Do not claim that a file exists before verifying it.
3. After saving, provide the actual file and a concise description of any limits in the report.

This constructed interaction separates useful assistance from an unsupported completion claim.