What the Data for This Category Looks Like
Typical entities in this category are bread and pastry, biscuit, instant noodle, and frozen pastry factories. Daily documents include formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection records. Data sources include raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, oven or frying line equipment, laboratory test results, and food traceability ledgers. Data updates follow production batches, each shift, or daily. Single-batch data is clearly linked to specific production links and material information.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
The data characteristics of this category create multiple constraints for this workflow. Frequent product line changes and diverse formulas require dialogue context to be accurately bound to the current production batch’s exclusive data. This prevents mixing feeding lists or cleaning records from different batches. Data updates occur per batch, shift, or daily. Real-time access to the latest production records and test results is required during dialogue. This prevents use of expired information for audits. Allergen control and cleaning switching are core risk points. Prompts must include fixed steps for allergen screening and cleaning verification. This ensures audits cover key links. Single-batch data links clearly to specific production links. Session identifiers must be bound to batch information. This avoids cross-batch session data confusion.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId Session Binding | Bind to the current production batch number | Ensure the session is linked to the production data and test records of the corresponding batch, to avoid cross-batch confusion |
messages Context Limit | Retain the last 20 rounds of dialogue history | Adapt to frequent product line change scenarios, avoid loading excessive redundant historical data that affects response speed |
variables Dynamic Binding | Bind to the current batch’s formula, cleaning records, and metal detection results | Based on the category’s data update feature by batch, ensure the dialogue can call the latest exclusive data in real time |
| Dialogue Timeout Threshold | 600 seconds | Adapt to the loading cycle of data such as laboratory test results and baking curves, avoid early termination of the dialogue |
| System Prompt Template | Include allergen screening, cleaning verification, and batch verification steps | Cover the core audit risk points of this category, ensure key checks are performed in each round of dialogue |
Three Common Configuration Errors
- Phenomenon: Dialogue response times exceed 10 seconds. Some scenarios time out and terminate. Cause: The context length of
messagesis not limited. All historical session data loads for every dialogue, leading to excessive model processing load. - Phenomenon: Dialogue logs cannot be queried by production batch. Cross-batch data confusion occurs. Cause:
chatIdis not bound to the production batch number. This creates a mismatch between the session and data batch, making it impossible to link corresponding records. - Phenomenon: Audit steps miss allergen screening or cleaning verification. Cause: Dynamic
variablesfields are not configured using category-specific documents. Prompts do not cover the category’s core risk check items.
How to Verify Correct Configuration
- Initiate a new batch audit dialogue. Check if the session identifier displays the current production batch number, to confirm
chatIdbinding is correct. - View the dialogue history record. Confirm that only the content of the last 20 rounds or fewer is retained, to verify that the
messagescontext limit is effective. - Trigger a dialogue step that requires calling production data. Check if the current batch’s formula or cleaning records are automatically loaded, to confirm
variablesbinding is correct. - Simulate a data loading delay scenario. Confirm that the dialogue timeout threshold setting meets business requirements and will not terminate the dialogue prematurely.
The values provided are common starting points and should be measured against the reader's own samples.