What the data for this category looks like
The data for education service intelligent due diligence reports primarily comes from educational institution qualification filing documents, teaching research achievement archives, student service ledgers, fee disclosure records, and industry supervision public platforms. Data update frequency varies by business segment: qualification-related information updates once annually, teaching research achievement data updates quarterly, and student service and fee data synchronizes weekly. The document structure divides into four modules: qualification verification, operational data, teaching research capability, and financial compliance. Each module includes fields such as unique identification number, validity period, and statistical values. Supported units include person-times, ten thousand yuan, calendar days, and others.
Constraints for Multi-turn Dialogue and Prompt Engineering
The multi-module structure of education service due diligence data requires multi-turn dialogue to trigger queries sequentially by qualification, operational, teaching research, and other modules. This prevents context overflow caused by loading full datasets at once. Differences in update frequencies across data categories require prompt engineering to distinguish calling logic for static qualification data and dynamic operational data. Only call annually updated filing information during the qualification verification stage, and use weekly synchronized student data during operational analysis. Fields include unique identifiers and multiple unit types. Prompt engineering must mandate that returned results include matching field names and units, to avoid unit confusion across module data. Data sources involve industry supervision platforms. Multi-turn dialogue must include a consistency verification step. Trigger supplementary verification when field conflicts appear between prior and subsequent query results.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Education service due diligence reports include multi-module fields, requiring complete context for qualification, operational, and teaching research data to avoid truncation of critical information |
recall_top_k | Top 8–12 results | Covers retrieval results for core due diligence modules, preventing omission of key field information |
similarity_threshold | 0.75–0.85 | Education service data has high field granularity, requiring a balance between retrieval precision and recall scope to avoid mixing irrelevant data |
history_message_limit | Previous 6 dialogue turns | Multi-turn interactions for education service due diligence mostly revolve around module switching; retaining the most recent 6 turns covers context requirements and avoids context overload |
system_prompt | For education service due diligence reports, ask questions sequentially across the four modules of qualification verification, operational data, teaching research capability, and financial compliance. Returned results must include field names and corresponding units, and verify data source consistency | Matches the module structure and field characteristics of education service data, guiding the model to output in compliance with specifications |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: When calling a custom retrieval tool, historical dialogue content is not included in the retrieval logic, and retrieval results do not include context-related information. Cause: The
history_message_limitparameter is not configured, or the context transfer switch is not bound in the tool call node, resulting in historical dialogue not being included in the retrieval context. - Phenomenon: After the workflow runs, the fields for yesterday's and today's dialogue logs are empty, and interaction records cannot be viewed. Cause: The workflow's dialogue log storage configuration is not enabled, or the storage path permission configuration is incorrect, resulting in logs not being written normally.
- Phenomenon: The model returns results normally during separate testing, but the workflow call fails, and there are response logs in the model backend. Cause: The parameter binding of the workflow node is incorrect, the model output is not correctly mapped to the input fields of subsequent nodes, or the timeout threshold is set too short, failing to cover the full data processing duration.
How to Confirm Proper Configuration
- Create a test dialogue, input multi-module questions related to education service due diligence, and verify that returned results are split by module and include field names and corresponding units.
- View the workflow's log storage path, and confirm that log files have been generated by time and include complete interaction content.
- Trigger a tool call test, and verify that the number of retrieved results matches the value range specified in the
recall_top_kconfiguration. - Test the parameter mapping of the model node and workflow node separately, and confirm that the model output can be correctly read by subsequent nodes.
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.