What Data for This Category Looks Like
Data for this category comes from raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory test reports. Daily documents include batching records, coating and calendering parameter tables, winding/stacking process records, liquid injection ledgers, formation and grading data, OCV and safety test reports. Data is updated in real time per production batch and per individual cell. Document fields include various process parameter values, equipment operating indicators, test results, and no additional statistical values.
What Constraints These Characteristics Impose on the "Multi-turn Dialogue and Prompt Engineering" Link
Since data is updated in real time per batch and per individual cell, multi-turn dialogue must accurately bind the context of the current production batch to avoid mixing parameters across batches. Most document fields are process values with clear units, so prompts must explicitly require the model to only call historical parameters for the corresponding batch, and not exceed the current process scope. Workshop data has strong real-time performance, so dialogue context must be limited to the current shift or batch range to prevent use of expired data. Additionally, electrochemical manufacturing processes have high requirements for parameter consistency; multi-turn dialogue must retain the process node keywords from the previous round to ensure follow-up questions revolve around the current process and avoid deviating from the production scenario.
How to Set the Configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current production batch | Ensure multi-turn dialogue context only associates with process data for the current batch, avoiding cross-batch confusion |
messages | Retain the last 15-25 turns of dialogue content | Cover complete process question scenarios for the current shift, while controlling context length to avoid timeouts |
variables | Inject the set of parameters for the current batch, such as batching, coating, etc. | Allow the model to directly call preset batch data without additional retrieval and matching |
| Maximum context length | 8000 token | Adapt to the length of complete process records for a single batch, preventing truncation of key process parameters |
| Question optimization switch | Calibrate based on actual measurements | Perform standardized conversion of users' colloquial process questions to match system document fields |
This configuration scheme is compatible with FastGPT v4.17.0.
Three Common Mistakes
- Cross-batch parameters are called during multi-turn dialogue. The symptom is that the reply includes coating areal density values not from the current batch. The cause is failure to bind the current production batch context via
chatId. - Historical questions in the dialogue are not retained. The symptom is that the model cannot associate the liquid injection dew point parameter mentioned in the previous round when following up. The cause is incorrect configuration of the
messagesparameter, failing to retain historical dialogue context. - The prompt outputs redundant thinking processes. The symptom is that internal model reasoning steps are mixed into the reply. The cause is failing to include a requirement to hide thinking processes in the system prompt.
How to Confirm the Configuration Is Properly Set Up
- Initiate a test dialogue, enter a question about the batching parameters for the current batch, follow up with a question about the coating parameters for that batch, and verify that the reply only associates with data from the current batch.
- View the dialogue history record, confirm that the
messagesparameter has retained the last 15-25 turns of dialogue content. - Trigger a follow-up question with parameters, verify that the reply does not include internal model thinking processes.
- Inject
variablesparameters for different batches, verify that the model only calls the currently injected batch data.
The values provided are common starting points and should be measured against the reader's own samples.