What this category of data looks like
This category of data originates from ERP/MES systems, VIN and vehicle configuration systems, tightening tools, EOL off-line inspection lines, and quality traceability systems. Daily documents include production orders, VIN lists, assembly work instructions, tightening torque data, off-line inspection reports, rework records, and consistency release documents. Data updates follow a per-vehicle schedule. Key tightening and inspection data sync in real time. Configuration lists and release documents update per shift. Documents use VIN as the unique identifier to link full-process data. Fields included are part numbers, torque parameters, inspection thresholds, and supplier batches.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
The full-process data linkage for individual vehicles centered on VIN requires multi-turn dialogue to precisely match the VIN identifier for the current conversation. This prevents cross-vehicle data confusion. Real-time synced tightening and inspection data have high update frequencies. Prompts must limit the context window to only the last 3 rounds of dialogue related to the current workstation. This stops redundant data from causing interference. The configuration complexity of mixed-line production requires the dialogue flow to guide step-by-step according to workstation order. Each round of prompts only opens access to work instructions and inspection standards for the corresponding workstation. This avoids information overload. Rework records and release document traceability requirements demand that dialogue contexts link historical interaction data for the corresponding VIN. This ensures Q&A results align with the full-process requirements of the individual vehicle.
How to configure the system
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the session ID to the current VIN | Use VIN as the unique identifier to distinguish individual vehicle conversations, preventing cross-vehicle data misuse |
messages | Retain 2-4 rounds of recent dialogue context | Individual vehicle process flows advance by workstation. Redundant context will interfere with process matching for the current workstation |
variables | Inject the current VIN, workstation number, and current shift | Quickly link the corresponding vehicle’s configuration, work instructions, and real-time production data |
| Maximum context window | 800–1200 characters | Control the volume of information input per dialogue, avoiding exceeding the model’s processing limits |
| Single recall limit | Top 3 matching documents | Individual vehicle process document volumes are limited. Too many recalled documents will distract the model |
Three common misconfiguration issues
- Symptom: No corresponding log is generated after a dialogue triggers an error. Troubleshooting of the abnormal step cannot be completed. Cause: Full session logging is not enabled. Only successful interactions are saved.
- Symptom: Prompts do not load before each round of user input. Cause: Prompt configuration is bound to a fixed trigger time, and it is not set to trigger before each dialogue round.
- Symptom: The dialogue record ID of the last AI reply cannot be accurately retrieved. Cause: The current session’s unique identifier is not bound via
chatId, so the session context cannot be accurately called.
How to verify the configuration is properly set up
- Enter a test VIN and workstation number, check if the dialogue context automatically links to the corresponding vehicle’s work instruction content.
- After initiating multi-turn interaction, check if the session list can distinguish dialogue records for different VINs via
chatId. - Trigger an abnormal error, check if a log file containing interaction details and error information is generated.
- For the shared application, check that different logged-in users can only see their own initiated dialogue records.
The values provided are common starting points and should be measured against the reader's own samples.