What this category of data looks like
This category of data comes from daily production documents of skincare, haircare, makeup, and special cosmetics manufacturers. Documents include formula process sheets, weighing and feeding records, emulsification and filling records, cleaning and site clearance records, semi-finished and finished product inspection reports, and label review materials. Data sources cover registered and filed formulas, raw material safety materials, production equipment, ERP/MES systems, laboratory data, and packaging material version information. Updates occur with each production batch or each line change. Fields include raw material components, feeding quantities, production parameters, inspection results, batch numbers, and packaging material information, among others.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
Data for this category is updated per batch or line change, and involves filing and compliance requirements. Multi-turn dialogue must be bound to specific production batch context to avoid mixing data across batches. Daily documents include multiple types of structured and unstructured fields. Prompt engineering must guide users to provide complete data for feeding, inspection, cleaning, and other production links in sequence. This prevents omission of key information such as formula components and restricted substance content. Data sources cover multiple systems. Prompt engineering must support associating ledger data from different sources to ensure consistency during summarization. It must also embed compliance check prompts in dialogue to reduce risks such as formula-filing mismatches and incorrect labeling.
Configuration Settings
The following configurations are based on FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Dialogue context window | Retain the most recent 20 messages | The volume of full-process dialogue data for a single production batch is moderate. 20 entries are sufficient to cover the complete question chain from feeding to finished product inspection. |
| Session identifier binding | Archive dialogue records by chatId | Matches the independent management requirements of production batches. This facilitates subsequent traceability of the ledger summary process for the corresponding batch. |
| Global variable configuration | Bind production batch numbers and workshop information from variables | Ensures each round of dialogue is associated with exclusive data for the corresponding production scenario. This avoids confusion of ledgers across different batches. |
| Tool trigger rule | Trigger when keywords related to formulas, inspections, or cleaning are detected | Automatically call ledger documents for the corresponding production link. This improves summarization efficiency. |
| Empty result handling | Prompt for supplementary key information when messages returns empty | Addresses empty result issues in actual usage. This avoids blank displays on the frontend. |
| Context length limit | Set to 8000–12000 token based on actual testing | Covers the complete content of multiple types of production documents. This prevents truncation of key compliance information. |
| Timeout setting | Set to 60 seconds | Matches the typical delay of data returned by production equipment and ERP systems. |
Three Common Misconfigurations
- Phenomenon: The AI dialogue node returns empty or an error prompt, and the frontend displays blank results. Cause: No branch processing logic is configured for empty
messagesor large model call failures. The original return result is directly passed through. - Phenomenon: When calling the chart tool to generate a production ledger chart, the chart displays as empty. Cause: The
variablesdata for the corresponding batch is not explicitly bound in the prompt engineering. The tool cannot obtain correct ledger parameters. - Phenomenon: Attempts to delete dialogue records fail, and the request returns an abnormal status code. Cause: The
chatIdparameter is not correctly associated. The system cannot locate the target dialogue record.
How to Verify Successful Configuration
- Initiate a query for a single batch production ledger. Check whether the dialogue context retains key information for that batch.
- Trigger a tool call. Verify that bound
variablesfield data can be correctly obtained. - Simulate a scenario where the large model returns empty. Check whether the preset error prompt logic is triggered.
- After initiating a dialogue, verify through the dialogue management interface that the
chatIdis correctly associated with the current session.
The values provided here are common starting points and should be evaluated against the reader's own samples.