What the Data for This Use Case Looks Like
Steel smelting ledger data originates from Manufacturing Execution Systems (MES), Programmable Logic Controllers (PLC), and offline quality inspection reports. Data updates follow two schedules: key process parameters refresh in real time every 10–30 minutes, while full ledgers compiled after each shift update daily. Each ledger entry includes a smelting batch number, furnace number, start and end times, core parameters for each process, material consumption, quality inspection indicators, and energy consumption data. Fields include molten iron carbon content, tapping temperature, smelting cycle, and more. Units adhere to industry standards such as ℃, min, t, m³, and others.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The multi-process parameters and clear batch identification of steel smelting ledgers create multiple constraints for multi-turn dialogue and prompt engineering workflows. A single batch ledger includes dozens of specialized parameters, so the context window must limit valid content length to avoid exceeding model processing limits. Parameters use industry-specific units and field names, so prompts must enforce standard naming and unit rules for fields to prevent non-compliant output. Real-time process data refreshes every 10–30 minutes. Dialogue calls must pull the latest data for the target batch in real time, and cannot rely on fixed caching. Additionally, when multiple users use the system, chatId must bind each user’s smelting batch context to avoid mixing data across different user conversations.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | First 8000–12000 characters | A single batch smelting ledger includes dozens of parameters, to avoid exceeding model processing limits |
chatId Binding Rule | Generate unique values using user identifier + smelting batch number | Distinguish conversation data for different users and different smelting batches |
messages Context Truncation | Retain the most recent 5–7 conversation turns | Filter redundant history and focus on the current ledger summary task |
variables Parameter Mapping | Bind furnace number and batch number as global variables | Auto-fill industry-specific fields in prompts |
| Prompt Template Validation | Calibrate based on actual testing | Adapt to field naming and unit rules for steel smelting ledgers |
| Dialogue Timeout Setting | 600 seconds | Allow full workflow for multi-process data pulling and model generation |
Three Common Mistakes
- The
Humanfield is empty in the API dialogue response preview. This occurs when the user message structure for themessagesparameter is not correctly passed, and the content field forrole: "user"is not filled as required. - Different users cannot view their own historical conversations. This occurs when the association between users and conversations is not bound via
chatId, leading all conversation data to share the same context. - Ledger summary results have inconsistent units. This occurs when the prompt does not explicitly specify steel industry-specific unit rules, leading the model to generate parameter units that do not meet production standards.
How to Verify Proper Configuration
- Send an API dialogue request with valid
chatIdandmessagesparameters, check if the response includes the correct user and assistant message structure. - Generate
chatIdusing different user identifiers and smelting batch numbers, send two dialogue requests, confirm that the contexts of the two conversations do not interfere with each other. - Input a query that includes industry-specific fields, check if the generated report automatically maps and fills parameters such as furnace number and batch number bound in
variables. - Set a 600-second timeout threshold, send a dialogue request that includes full ledger data, confirm the dialogue is not interrupted by timeout and returns the complete summary result.
The values provided here are common starting points, and should be measured against the reader’s own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.