This guide uses FastGPT v4.17.0.
What data for this category looks like
Business data for construction engineering quality and safety supervision mainly comes from the engineering quality and safety supervision system, on-site construction real-name management platform, supervision logs, and test reports. Data updates weekly. Daily generated documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Document fields include project number, participating unit information, inspection item details, rectification requirements and closed-loop status, and more. Individual document content lengths vary widely, ranging from several hundred-word rectification notices to several thousand-word completion acceptance reports.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data for this category comes from multiple dispersed sources. Multi-turn dialogue must integrate field information from multiple channels including engineering supervision systems and real-name management platforms. Context synchronization logic must be configured to match the weekly update rhythm. Daily document types are varied. Prompts must preset dedicated field extraction and verification rules for different documents such as supervision inspection records and hidden danger rectification notices. On-site construction supervision is the core scenario. Multi-turn dialogue must guide users to provide on-site inspection details step by step, to avoid overloading single-turn input. Business errors have serious consequences. Prompts must clearly state the rigor requirements for output content, to ensure no key information is omitted.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context | Most recent 20 dialogue history turns | Business data updates weekly, overly long history has no practical reference value, prevents model context overload |
variables | Bind preset fields such as project number and rectification deadline | Matches the fixed field requirements of daily documents, ensures accurate and compliant information called by prompts |
chatId | Bind single supervision task ID | Isolates dialogue contexts for different projects, prevents information confusion across tasks |
| Single-turn input limit | 800–1200 characters | Adapts to the typical content length of a single hidden danger rectification notice, prevents input information overload |
| Question optimization | Enabled, calibrated based on actual testing | Optimizes question logic for engineering professional terminology, improves model understanding accuracy of supervision business |
messages | Split dialogue nodes by task type | Differentiates dialogue contexts for different links such as supervision inspection and hidden danger rectification, simplifies model input logic |
Three Common Misconfigurations
- Dialogue history fails to retain specified reply content, and auto-replies are empty when the dialogue is reopened for the second time. Cause: The
chatIdparameter is not bound, so the system cannot associate the dialogue context for the corresponding supervision task. - Calling system time variables in prompts fails to accurately obtain the current date. Cause: The
variablesparameter is not correctly configured to bind platform time variables, or the variable call format does not meet system requirements. - Abnormal prompts appear during voice input dialogue, and audio-to-text conversion cannot be completed normally. Cause: The container deployment environment is not correctly configured with ffmpeg dependencies, and sufficient permissions to complete dependency installation are not obtained.
How to Verify Proper Configuration
- Initiate a simulated supervision inspection dialogue, check whether the context retains the historical replies for the current task, and verify whether
chatIdis associated with the correct supervision task ID. - Insert a system time variable into the prompt, initiate a test dialogue, and confirm whether the document date matches the current date.
- Configure a workflow multi-turn dialogue node, input test data, and check whether the final output only includes the processing results for the target link.
- Upload a single supervision inspection record, test whether the input length of the multi-turn dialogue meets the preset limit, with no truncation or abnormal prompts.
The values provided here are common starting points, and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.