What the data for this category looks like
Loan backlog data primarily comes from core credit systems, repayment flow modules, and collection record databases. The update cadence is daily T+1 batch synchronization of full existing inventory and newly added backlogs from the current day. Each individual document includes fields such as customer unique identifier, contract number, remaining principal, current overdue days, monthly repayment plan, historical collection frequency, and risk warning tags. Remaining principal is measured in Renminbi yuan, overdue days are counted in natural days, and repayment plans are split into detailed entries by repayment date.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Because data updates daily via T+1 synchronization, multi-turn dialogue must pull the latest backlog snapshot during each interaction to avoid generating conclusions using expired data. Nested repayment plans and time-stamped collection records require prompts to explicitly specify field extraction hierarchy and time-sorting rules, ensuring output matches the latest backlog status. Risk verification needs that involve multiple associated fields require triggering field verification step-by-step in the dialogue flow. This prevents overloading a single prompt with too much field verification logic and avoids context overflow.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Loan backlogs contain multiple fields and nested repayment plans, requiring sufficient context to carry complete data and interaction history |
recall_top_k | Top 3–5 historical dialogue entries | Risk judgment needs to associate recent verification results; excessive historical content will dilute core backlog information |
prompt_template | First extract the core fields of the current backlog, then associate historical collection records based on overdue status, and finally output risk grading conclusions | Guide the model to process nested data step-by-step, avoiding overloading a single prompt with too much field verification logic |
max_tokens | 2000–3000 characters | Risk conclusions need to include multi-field verification results; limiting output length ensures focused information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Batch-imported backlog repayment plan details are lengthy, requiring sufficient time to complete structured parsing |
temperature | 0.1–0.3 | Risk control scenarios require stable and consistent judgment logic, reducing randomness |
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 targeted analysis, and it is recommended to test against your own samples before finalizing.
Three common mistakes
- Phenomenon: In the loan backlog text extraction phase, the selectable model list does not include the configured GLM series models. Cause: Structured extraction permissions for the corresponding models are not enabled in system configuration, only model call permissions for the dialogue module are enabled.
- Phenomenon: Only a single model can be selected as the call source for multi-turn dialogue nodes in workflow orchestration. Cause: Multi-model linkage configuration for the workflow is not enabled, and single-model call is restricted by default.
- Phenomenon: After enabling the file upload function on the frontend, uploading a backlog file via the API interface returns a 400 error. Cause: The file upload field name consistent with the frontend configuration is not included in the API request, causing the interface to fail to parse the uploaded backlog data.
How to confirm successful configuration
- Initiate a test dialogue, input a fragment of the latest backlog data, and verify whether the fields returned by the model match the core input information.
- Enter the model configuration page, check whether the selectable model list includes the deployed non-GPT series models, and confirm that the permissions for text extraction and dialogue modules have been enabled synchronously.
- Configure a workflow node, enable the option to hide intermediate outputs, initiate a test call, and confirm that only the results of the final dialogue are displayed on the frontend interface.
- Call the file upload API, include the field name and file type consistent with the frontend configuration, and check whether the parsed results returned by the interface include the core fields of the backlog.
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.