What this category's data looks like
This category's data covers daily production documents from bread and pastry, biscuit, instant noodle, and frozen pastry factories, including formulas and feeding lists, production records, baking/steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data is sourced from raw and auxiliary material qualification certificates, ERP/MES systems, weighing equipment, ovens/frying lines, laboratory testing, and food traceability ledgers. Updates occur per production batch, per shift, or daily. Document fields include material names, feeding quantities, process parameters, test results, and some documents require association with allergen labels and shelf life information.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
Production data for this category is updated per batch and has high correlation. Multi-turn dialogue must limit the context scope to avoid cross-batch data interfering with result accuracy. High-frequency scenarios involving allergens and cleaning switching require prompts to accurately identify material labels and job scenarios. Fixed parameters for the current production batch must be passed via variables. Document fields include multiple types of units such as baking temperature, duration, and feeding quantity. Multi-turn dialogue must unify field parsing rules to prevent unit confusion. Exception reports require quick access to recent batch test records, so prompts must clearly limit the time and batch range for data retrieval to avoid redundant data affecting drafting efficiency. Additionally, multi-turn dialogue must support step-by-step retrieval of production data to adapt to the frequent line change business characteristics of this category.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context | 1000–1500 preceding characters | Single-batch production document volume for this category is moderate; limiting context avoids redundant historical data interfering with current drafting |
messages | Retain the last 3 rounds of dialogue and filter non-current-batch content | Data for this category is updated per batch; filtering irrelevant dialogue ensures prompts focus on current production tasks |
variables | Bind current batch ID and daily allergen list | Accurately pass fixed parameters to avoid prompts missing the unique allergen control requirements of this category |
| Single Search Limit | Top 5 entries | Data for this category is grouped by batch; retrieving a small amount of intra-batch data at one time meets the drafting needs of process documents and exception reports |
chatId | Bind production task ID | Isolate dialogue contexts for different production tasks to prevent cross-batch data confusion |
| Dialogue Timeout | 600 seconds | Single-batch production process duration for this category is long; timeout setting must adapt to production rhythm |
| Question Optimization | Calibrate based on actual testing | Adapt to the dialogue logic of FastGPT v4.17.0 to meet the complex query needs of multi-link parameters for this category |
Three Common Mistakes
- Phenomenon: Question-and-answer effect deteriorates after multi-turn dialogue, and results are accurate after a new dialogue is opened. Cause: The current production task is not bound via
chatId, and context is mixed with historical data from other batches, causing the prompt to associate incorrect information. - Phenomenon: Production data results from SQL queries cannot be directly displayed in the AI dialogue box. Cause:
variablesare not configured to pass field mappings for query results, and the prompt does not explicitly require formatting results into a format compatible with the dialogue box. - Phenomenon: Multi-turn dialogue cannot send two related messages at once, or Markdown-formatted exception reports cannot be hidden on specified channels. Cause: Dialogue message queue rules are not configured correctly, and the prompt does not specify Markdown format display scenarios, resulting in format rendering that does not meet requirements.
How to Confirm the Configuration is Correct
- A query including the current production batch number is submitted, and the dialogue context is verified to only contain production data for this batch, with no redundant information from other batches.
- A workflow including an SQL query is run, and the query results are confirmed to automatically map to a structured format displayable in the AI dialogue box.
- The multi-turn dialogue process is triggered, and the display effect of sending multiple related messages and Markdown-formatted content at once is tested.
- The multi-turn process parameter translation process is tested, and the system is confirmed to retrieve production data step by step and complete the translation.
The values provided here are common starting points and should be measured against the reader's own samples.