What data for this category looks like
Data for this category originates from electromagnetic design databases, copper wire and insulating material batch ledgers, winding equipment logs, balancing machine collected data, electrical test bench reports, and ERP/MES systems. Data is updated per finished unit, material batch, or each design modification. Core documents include winding process cards, insulation treatment records, dynamic balance reports, dielectric strength test records, and type and factory inspection reports. Data fields include winding turns, insulation layer thickness, dynamic balance residual deviation, dielectric strength test voltage values, temperature rise test results, and are linked to corresponding production batches and equipment numbers.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering workflow
The batch-based and equipment-linked nature of this category's data requires that multi-turn dialogues be bound to the corresponding production batch and equipment ID to avoid mixing data across batches. Professional fields such as winding turns and dielectric strength test voltage must be clearly standardized in prompts to prevent the LLM from generating content that does not conform to industry terminology. Data is updated frequently and bound to a single product or single batch of materials, so dialogue context must limit valid turns to avoid loading expired, non-current batch data. At the same time, quality audits involve serious consequences such as winding burnout and insulation breakdown. Prompts must constrain the LLM to generate conclusions only based on uploaded test reports and process card content, and not add unverified information on its own.
How to configure the setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the current production batch ID | Distinguish audit dialogues for different batches to avoid data confusion |
messages | Retain the most recent 4 turns of dialogue context | Match the multi-step audit process for each product batch, avoid loading redundant data |
variables | Preset field variables such as winding turns and dielectric strength test voltage | Standardize professional data fields called by the LLM |
| Maximum context length | 800–1200 characters | Adapt to the core content length of a single test report |
| Single recall limit | Top 3 documents | Match the number of core documents for a single batch audit |
| Question optimization | Calibrate based on actual measurements | Adapt to professional terminology usage habits in the motor manufacturing industry |
Three common mistakes
- Phenomenon: The model version displayed in the dialogue and workspace is inconsistent, and the actual called model does not match the configuration. Cause: Correct model parameters are not bound via
variables, leading to inconsistent model calls across different scenarios. - Phenomenon: The LLM generates content beyond the knowledge base, and the reply scope is not strictly limited. Cause: The prompt does not clearly bind the exclusive document sources of this category, such as winding process cards and test reports.
- Phenomenon: Only the most recent dialogue is analyzed, and the batch context of multi-step audits cannot be associated. Cause: The
messagesconfiguration is incorrectly set to retain only 1 turn of dialogue context, losing earlier batch ID and process configuration parameters.
How to confirm the configuration is correct
- Launch a dialogue bound to a production batch ID, check if the dialogue context only loads document data for the corresponding batch.
- Enter an audit question related to winding turns, check if the reply content only comes from preset process cards and test reports.
- Launch two or more audit dialogues, check if
messagesretains earlier batch and parameter information. - Upload a voice format test file, check if the interface supports voice-to-text and integrates it into the current dialogue process.
The values provided here are common starting points and should be evaluated against the reader's own samples.