What data for this category looks like
This category of data covers documents including coating process cards, paint batch sheets, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, VOCs ledgers, and more. Data sources include paint supplier TDS/SDS, data collected from spray booths and ovens, colorimeters, film thickness gauges, environmental monitoring equipment, and MES systems. Updates occur per paint batch, per work shift, and via continuous real-time collection. Fields include paint batch numbers, film thickness values, oven temperatures, VOC emission concentrations, and other metrics, using standard industry-specific units.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
Data sources for this category are scattered, and update frequencies vary. Multi-turn dialogue must associate ledgers from different sources by batch. Prompts must clearly distinguish the priority of real-time collected data versus batch documents, to avoid mixing data across batches. Document types cover process, inspection, compliance, and other categories. Prompts must specify extraction fields for corresponding documents, to prevent mixing fields from VOCs ledgers and film thickness reports. Data association must match pre-treatment, spraying, and curing windows. Multi-turn dialogue must gradually guide the supplementation of associated information, to ensure logical consistency of data across all stages. Additionally, errors may lead to appearance repairs and compliance risks. Prompts must include validation logic for defect repair orders, to ensure summary results cover both quality and compliance dimensions.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context | Previous 15 dialogue turns | Coating ledgers update per batch. Excessive history will confuse paint mixing and oven data for the current batch |
| Single Search Limit | Top 6 knowledge base documents | Covers core document types including paint batch sheets and film thickness reports, avoids interference from redundant data |
| Question Optimization | Enabled | Automatically organizes professional terminology related to coatings, unifies wording for fields such as film thickness and VOCs |
chatId | Bound to current coating batch ID | Ensures multi-turn dialogue only associates ledger data for the corresponding batch, prevents cross-batch confusion |
variables | Pre-set paint batch number, oven temperature threshold | Reduces repeated input, improves summary efficiency |
messages | Stitched and grouped by document type | Organizes dialogue context in the order of process cards and inspection reports, aligns with business logic |
Three common mistakes
- All output results from AI dialogue nodes are displayed after workflow runs, but only the final entry should be retained. Cause: No context truncation rule configured for the
messagesparameter, and no restriction set to only retain dialogue turns for the current coating batch. This causes ledger data from previous batches to be included. - After connecting external dialogue channels, generated report content contains large amounts of meaningless punctuation such as # and *. Cause: Question optimization configuration is not enabled, and no standardization processing is applied to input formats for professional terminology. This causes format markers from original prompts to be retained.
- After calling the dialogue interface, the knowledge base document IDs referenced in this conversation are not returned. Cause: No batch ID is configured as a retrieval filter condition in
variables, making it impossible to track specific referenced ledger documents.
How to confirm configurations are correct
Run a query covering multiple batches of paint ledgers. Verify that dialogue context only retains historical information for the current batch, confirming the maximum context configuration is active. Input professional terminology related to coatings. Verify that generated content unifies wording for fields such as film thickness and VOCs, confirming the question optimization configuration is active. Review interface return content. Confirm that knowledge base document IDs referenced in this conversation are included, verifying binding configurations for variables and chatId. Connect an external dialogue channel to initiate a query. Verify that generated reports have no redundant punctuation such as # and *, confirming format standardization configuration is active.
The values provided are common starting points, and should be evaluated against the reader's own samples.