What this category of data looks like
Core data for construction engineering quality and safety supervision comes from the engineering quality and safety supervision system, on-site real-name registration platform, supervision logs, and test reports. The update frequency is weekly. The system produces daily documents and materials including supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Documents follow structures that cover fields such as project basic information, on-site inspection content, hidden danger rectification requirements, and acceptance judgment standards. For example, rectification notices include hidden danger number, responsible entity, and closed-loop deadline; test reports include test items, measured values, and qualified judgment results. No additional fictional statistical data is included.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
Weekly updated data sources require the dialogue tool to synchronize latest supervision system and on-site platform data regularly. This prevents reduced document accuracy caused by expired information. Multiple document types have differentiated fields. Prompt engineering must accurately match the format and field requirements of each corresponding document to avoid confusing rectification notices and completion acceptance reports. Long documents such as complete supervision logs and batch test reports exceed model context limits. Configure chunking processing to adapt to model constraints. Multi-turn supervision processes require continuous context retention. Dialogues must retain historical context to link previous inspection records and rectification closed-loop status. This ensures generated documents align with full project progress.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | Covers the total length of multiple types of documents updated in a single week, adapts to the average length of construction engineering documents, and complies with the parameter limit of FastGPT v4.17.0 |
| Document chunk length | 1000–1500 characters | Adapts to the integrity of single-block information for test reports and supervision logs, avoids overly fragmented single-block content |
| Document chunk overlap length | 200–300 characters | Retains contextual association between chunks, avoids key information breakage after slicing |
| Hide tool call output | Enabled | Only displays the final AI-generated document content to supervision personnel, reduces redundant tool call logs |
chatId | Enable session binding | Binds multi-turn dialogue context for a single project, associates historical supervision records and rectification status |
messages | Retain the last 20 dialogues | Balances context length and relevance of historical information, adapts to the multi-turn process requirements of construction engineering supervision |
variables | Bind project number, supervision station number | Achieves context isolation for multi-project dialogues, avoids information confusion between different projects |
Three common mistakes
- After initiating a tool call to generate a hidden danger rectification notice, the interface displays both the original tool call request and response content. This occurs because the hide tool call output configuration is not enabled, causing redundant information to interfere with supervision personnel's reading.
- After uploading a single complete supervision log, the dialogue returns a context overflow error prompt. This occurs because document chunking parameters are not configured, and over-length documents are not sliced, exceeding the model-supported context length.
- When initiating a second follow-up question about rectification closed-loop, the AI does not associate the hidden danger content in the previous inspection record. This occurs because the
messagescontext retention configuration is not enabled, or the context length is set too small to load historical dialogue information.
How to confirm the configuration is correct
- Initiate a test dialogue that includes tool calls, verify that the returned result only contains the AI-generated document content, with no original tool call logs.
- Upload a single over-length supervision log with a length exceeding 10,000 characters, check the automatically generated chunked logs in the system, confirm that the number and length of chunks comply with the configuration requirements.
- Initiate two linked supervision dialogues, for example, first query the supervision inspection record of a project, then follow up on the rectification status of the corresponding hidden danger, verify that the AI can associate historical dialogue content to generate an accurate reply.
- View the system log archive configuration page, confirm that the archive duration and storage limit meet business requirements, and that manual cleanup operations can be performed.
The configuration values listed above are common starting points. Adjustments should be made based on the user's own specific samples and business requirements.
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.