Data Overview for This Category
Data for this category comes from design documents, standard instruments and environmental monitoring data, calibration software, laboratory information systems, and customer range requirements. Daily generated materials include assembly and commissioning sheets, calibration procedures, original calibration records, error curves, measurement certificates, and software version records, which are updated after each calibration of every device. Data fields include device ID, calibration date, standard instrument ID, ambient temperature and humidity, multi-point measurement error values, qualified judgment results. The unit of the error curve corresponds to the measured parameter, such as kPa for pressure and ℃ for temperature.
Constraints Imposed on Multi-turn Dialogue and Prompt Configuration
The high-frequency updates, coexistence of multiple document types, and strict traceability requirements of this category of data impose clear constraints on multi-turn dialogue and prompt configuration. Each calibration of a device generates an independent ledger, which requires binding the session to the exclusive data of the corresponding device via chatId to avoid cross-device data confusion. Multiple document types include structured records, curve charts, and certificate files. Prompts must clearly distinguish the parsing rules for different documents to avoid format confusion. High-frequency updated data requires that the session context be limited to an effective range to prevent old data from interfering with the current ledger summary logic. At the same time, traceability chain verification for calibration records must be embedded in prompt rules to ensure that generated reports meet metrological audit requirements.
Configuration Settings
This document is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Session Context Retention Duration | 1200–1800 seconds | Matches the update cycle of each calibration for every device in this category, prevents old ledger data from interfering with the current session |
| Number of Messages Loaded per Single Dialogue | Top 8 | Limits the loading scope of messages to prevent context information overload |
| Variable Binding Rule | Associate the device ID field in variables | Accurately binds exclusive ledger data for the corresponding device to avoid cross-device confusion |
| Prompt Validation Rule | Embed traceability chain verification logic | Complies with mandatory requirements for metrological audits in this category |
| Historical Message Filter Criteria | Retain only content related to the last 3 calibrations | Focuses on the current session's ledger summary and report generation needs |
Common Misconfigurations
- Phenomenon: AI thinking process is displayed in the conversation history, leading to redundant ledger reports. Cause: The thinking process display switch is not turned off in system settings, and the requirement to hide the thinking process is not explicitly stated in the prompt.
- Phenomenon: Cross-device ledger data appears in generated reports, and field matching is incorrect. Cause: The session is not bound to the
variablesof the corresponding device viachatId, resulting in chaotic data association. - Phenomenon: Some measurement point values are missing from the summarized error curve data, and the number of results does not meet expectations. Cause: The prompt does not explicitly require extracting error data for all measurement points, or the number of loaded context messages is insufficient.
How to Verify Correct Configuration
- A calibration ledger query for a single device is initiated. The returned data is verified to only include records for that device, with no other device data mixed in.
- After a calibration report is generated, the report is checked to confirm it includes mandatory fields required for metrological audits such as traceability chains and environmental conditions.
- The conversation history is reviewed to confirm no AI thinking process content is displayed, and only user questions and final report results are retained.
- Multiple ledger queries for the same device are initiated. The returned measurement point data is verified to be complete with no omissions.
The values provided are common starting points and should be measured against the reader's own samples.