What the data for this category looks like
Educational service financial report data comes from publicly disclosed periodic reports, with fixed quarterly or annual update cycles. Document structures include fields such as core business segment revenue, teaching and research investment costs, number of enrolled students, total class hours, and revenue per customer. Units are mostly RMB ten thousand, person-times, and class hours. Different educational service institutions have varying report formats and field order. Some institutions also disclose data on new course quantities and cooperative education projects.
What constraints these characteristics impose on multi-turn dialogue and prompting
Inconsistent fields and formats of educational service financial reports require multi-turn dialogue to first align an institution’s report fields. Prompts must clearly define the queried report period to prevent the model from confusing business fields unique to education services. Long financial report documents require limiting the context window size during multi-turn dialogue to avoid excessive token consumption. The fixed data update cycle requires multi-turn dialogue to support filtering by specified report periods, ensuring queries use the latest disclosed reports.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Educational service financial report documents have long length; limiting the context window prevents token overflow |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Educational service financial report files may contain multiple pages of data, leading to longer parsing time |
relevantCount | Top 3–5 entries | Educational service financial reports have many fields; accurately recall relevant report snippets |
Segment Length | 800–1200 characters | Split long financial report documents to adapt to model input length limits |
workflow_display_mode | Only show the output of the last AI node | Matches the single-round focused requirement of educational service financial report analysis |
prompt_prefix | Only process fields related to educational service financial reports, exclude non-business data | Clarify that the model only handles report fields unique to educational services |
The parameter values provided on this page are common 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 mistakes
- Phenomenon: The workflow runs and displays multiple AI dialogue results. Cause: Did not set
workflow_display_modeto only show the output of the last AI node, retaining the default multi-node output configuration. - Phenomenon: The AI-generated financial report analysis content is not properly polished and expanded. Cause: Did not add clear instructions for polishing and expansion in
prompt_prefix, or did not pass dialogue context. - Phenomenon: When connecting to external dialogue channels, the output content contains a large number of punctuation marks such as # and *. Cause: Did not configure markdown rendering adaptation for external channels, causing the format markers in the model output to not be parsed correctly.
How to confirm the configuration is correct
- Upload a financial report document of an educational service institution, initiate multi-turn dialogue, check whether the dialogue context conforms to the configuration range of
maxContext, and verify that the parsed document fragments are accurate. - Trigger the workflow run, confirm that the interface only displays the output result of the last AI node, verifying that the
workflow_display_modeconfiguration takes effect. - Add a polishing and expansion instruction to the dialogue, check whether the AI output content meets the expected expansion effect.
- Connect to an external dialogue channel, send a test request, check that the output content has no redundant format markers.
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.