What the Data for This Category Looks Like
General equipment yield rate and market data come from official statistical monitoring systems of the machinery and equipment manufacturing industry, public industry ex-factory price market databases, and enterprise-disclosed production capacity operation data. This data supports market daily report requirements for financial scenarios. Data updates follow a monthly core cycle; some high-frequency monitored categories update weekly. Documents use structured table format, including fields such as category identifier, statistical cycle, benchmark ex-factory price, production capacity utilization rate, and revenue share. Benchmark ex-factory price uses yuan per unit or yuan per kilowatt as its unit. Production capacity utilization rate appears as a ratio, and revenue share uses a decimal format.
What Constraints Do These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering?
For financial scenario yield rate market daily reports, general equipment data characteristics create multiple constraints for multi-turn dialogue and prompt configuration. The monthly core update cycle requires prompts to include the statistical cycle parameter as a mandatory field. This prevents the model from calling outdated or cross-cycle data. The structured table’s multi-field structure requires prompts to clearly specify which fields to extract. Without this, accurate matching of target yield data is impossible. High-frequency weekly monitored categories require multi-turn dialogue to support dynamic statistical dimension switching. It also requires limiting context window length to avoid mixing data from different cycles. Decimal-formatted yield fields require prompts to clearly define output format rules. This stops the model from returning non-compliant numerical values. The category identifier field requires dialogue flows to retain user-specified category information. This prevents cross-category data confusion.
How to Configure the Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | General equipment data documents are mostly structured tables, requiring sufficient context to match field and cycle parameters and avoid truncating critical information |
prompt_template | Must include the three required variables: "statistical cycle", "category identifier", and "target field" | General equipment data has multi-cycle, multi-category, and multi-field characteristics; mandatory binding of variables can prevent the model from extracting incorrect data |
multi_turn_memory_size | 3 dialogue turns | Yield queries for general equipment often require clear cycles and categories; 3-turn memory retains consecutively specified parameters from the user and avoids repeated input |
data_refresh_threshold | 7 days | General equipment data is mostly updated monthly; setting a 7-day threshold ensures that the latest released data is called and avoids using outdated information |
response_format | Only return the values of specified fields, retain the original decimal format | General equipment yield fields are in decimal form; clarifying the format avoids the model returning non-required expressions |
field_mapping_rule | Precisely match according to the document header names | General equipment data document fields are fixed; precise mapping ensures that the extracted fields match user requirements |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: After calling the API and passing prompt variables, the returned result does not match the specified general equipment category. Cause: The
prompt_templatefails to correctly bind the variable placeholder, or the passed variable format does not match template requirements. - Phenomenon: Multi-turn dialogue returns empty or incorrect fields when extracting general equipment data. Cause: The
field_mapping_ruleis not set to precise header matching, or the prompt does not clearly specify the field to extract. - Phenomenon: The model returns general equipment yield data with incorrect formatting, such as extra unit annotations. Cause: The
response_formatis not configured to retain the original decimal format, or the prompt does not clearly limit output content scope.
How to Confirm the Configuration Is Complete
- Upload a sample structured data document for general equipment, run a test query, and check if the returned result matches the specified statistical cycle and category identifier.
- Pass preset prompt variables via the API, verify that returned content correctly substitutes variables with no missing or misaligned data.
- Run consecutive multi-turn dialogues, specify different general equipment categories and statistical dimensions in sequence, and check if the system retains context and returns matching data.
- Check parameter saving status in the configuration interface, confirm all configuration items saved successfully with no abnormal prompts.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.