What Data for This Category Looks Like
Data sources for this category include MES systems, online testing equipment, laboratory test results, process parameter libraries, customer quality standards, and historical defect libraries. Data is updated in real time per production batch or per shift, or synced continuously. Daily documents include control plans, patrol inspection records, SPC reports, finished product inspection reports, nonconforming product review forms, release forms, and quality monthly reports. Fields cover inspection item numbers, defect levels, release thresholds, and batch numbers. Units include pieces, ppm, and similar units.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The real-time update feature of data requires that dialogue contexts do not retain overly long history, to avoid use of expired batch parameters or inspection standards. Differences across multiple document types require prompt engineering to clearly distinguish call rules for different documents, such as separating field requirements between control plans and nonconforming product review forms. Specific unit requirements for fields require prompt engineering to enforce matching the unit format of inspection items, to avoid outputting non-compliant values. Associated queries for historical defect libraries require multi-turn dialogue to gradually narrow the scope, ensuring accurate identification of abnormal impact ranges.
How to Configure Settings
The following configurations are based on the functional logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 800–1200 characters | Matches the average length of single-batch process documents, avoids loading expired historical data |
| Number of retained conversation history entries | First 3 turns of dialogue | Corresponds to core workflow steps for single-batch inspection, reduces redundant data usage |
variables binding rules | Bind batch inspection standards and defect library fields | Ensures generated process documents or exception reports match current batch customer requirements |
chatId configuration | Bind production batch number as unique identifier | Separates dialogue contexts for different production batches, ensures data isolation |
| Context refresh timing | Triggered when switching batches | Matches production batch update rhythm, avoids mixing cross-batch data |
Three Common Misconfigurations
- Symptom: SQL query results returned by the dialogue cannot be correctly embedded into AI reply text. Cause: The prompt engineering does not explicitly require splicing query results bound in
variablesin a specified format, leading to incorrect variable rendering. - Symptom: Answer accuracy declines after multiple turns of dialogue, and returns to normal after restarting the dialogue. Cause: The context history length is not limited, causing expired batch data or redundant dialogue content to interfere with logical judgment of the current request.
- Symptom: Unable to clear dialogue context records as required. Cause: The
chatIdis not bound to the production batch, leading to incorrect triggering of context data from other batches, or the context refresh trigger rule is not configured.
How to Verify Correct Configuration
A single-batch process document drafting dialogue is initiated manually, and the reply is checked for inclusion of the current batch’s inspection item numbers and corresponding thresholds. A dialogue is initiated for another production batch, and the context is confirmed to not carry parameter information from the previous batch. An exception report generation request is submitted, and the reply is checked for association with similar defect records from the historical defect library. The dialogue context panel is viewed, and confirmation is made that only the most recent 3 turns of dialogue content for the current batch are retained.
The values provided are common starting points and should be measured against the reader's own samples.