What the data for this category looks like
Data for state-owned large bank intelligent due diligence reports primarily comes from official public annual reports, quarterly operational briefings, regulatory submission archives from the banking and insurance regulatory authority, and internal credit due diligence archives. The data update schedule follows full annual updates for annual reports, incremental quarterly updates for operational briefings, and irregular synchronization of regulatory documents per regulatory requirements. Most documents use structured formatting, including fields such as basic entity information, asset and liability scale, business revenue, and risk exposure. All field units uniformly use currency units such as hundreds of millions of yuan and ten thousands of yuan. A single complete due diligence report can span dozens of pages.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Dispersed data sources, inconsistent update schedules, and long document lengths for state-owned large bank due diligence reports create multiple constraints for multi-turn dialogue and prompt configuration. First, multi-turn dialogue must support gradually clarifying due diligence entities and data cycles to avoid information confusion across institutions or cycles. Second, prompts must clearly define data update scopes to match the different update rhythms of annual and quarterly reports, preventing the use of outdated content. At the same time, long document structures require multi-turn dialogue to retain contextual associations, avoid single-call information overload, and enforce field unit checks to ensure output results match the units used in the reports.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | State-owned large bank due diligence report documents are lengthy, so full context for multi-turn dialogue must be retained to avoid loss of critical information |
Recall count | Top 8–12 entries | Due diligence reports contain multiple structured fields, so enough key information must be covered while avoiding interference from redundant content |
chunk_size | 1500–2000 characters | Adapt to the structured paragraph length of due diligence reports, balancing precision and completeness of segmented retrieval |
Similarity threshold | 0.75–0.85 | Accurately match field information in due diligence reports, filtering irrelevant non-due diligence content |
conversation_timeout | 300 seconds | Multi-turn due diligence queries require repeated information verification, so sufficient response and processing time must be reserved |
SYSTEM_PROMPT | Explicitly specify the due diligence entity and data cycle, ensure output matches the units used in the report | Avoid information errors across entities or cycles, ensure output aligns with report formatting |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: AI dialogue steps return a 422 status code. Cause: Context parameters for the due diligence report are not correctly bound during multi-turn dialogue, triggering interface validation rule interception.
- Issue: Retrieval content does not match expectations when title separators are used for long documents in the knowledge base. Cause: State-owned large bank due diligence reports have complex structured title hierarchies. Using a single-level title as a separator causes segmentation logic to mismatch the document structure.
- Issue: The new conversation initial popup continues to display after private deployment. Cause: Default configuration values for popup triggering in the front-end code have not been modified, causing the component to not be disabled as required.
How to Confirm Configurations Are Correct
- Initiate a due diligence field query targeting a specified state-owned large bank, verify that the units of returned content match the field units used in the report.
- Trigger multi-turn dialogue, adjust the query scope and data cycle in sequence, confirm that contextual associations do not show information confusion across entities or cycles.
- Upload a single complete due diligence report, check that the length of segmented results matches the
chunk_sizeconfiguration value. - Simulate multi-turn queries, wait longer than the
conversation_timeoutconfiguration duration, confirm that the dialogue session terminates normally.
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.