What the Data for This Category Looks Like
The data for this category primarily originates from ticketing systems, customer interview records, CRM customer profiles, and product roadmaps. Update frequency follows a weekly cadence or per version release cycle. Daily associated documents include requirement pool lists, requirement evaluation forms, customer feedback summaries, and version planning descriptions. Core fields include demand source customer, priority, merge status, corresponding R&D schedule, and similar items. No unified mandatory format exists, but the data must align with internal requirement management workflows.
Constraints Imposed on Multi-turn Dialogue and Prompt Templates
Data sources for this category are scattered, with a fixed update rhythm. Multi-turn dialogue must associate context information across systems to avoid fragmented requirement judgments. Non-uniform document structures require prompt templates to adapt to requirement records in multiple formats. Prompt templates must also be regularly calibrated using requirement pool data updated with each version. The highly time-consuming steps of duplicate requirement merging and value evaluation require multi-turn dialogue to support gradual screening and comparison of historical feedback, to avoid information overload in a single dialogue session.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window configuration | Retain the first 8 rounds of dialogue + requirement documents from the last 2 versions | Matches the weekly or per-version update cadence of this category, retains recent valid requirement context |
| Variable binding rules | Bind customer ID and requirement status fields in variables | Associates customer requirement data from CRM and ticketing systems, enabling precise context matching |
| Multi-turn dialogue timeout setting | 600 seconds | Covers the cross-turn communication duration for requirement evaluation and merging, avoids mid-session interruptions |
| Prompt template loading rule | Load preset requirement pool templates in the order of roles in messages | Adapts to the fixed structure of requirement pool documents, transmits customer feedback and evaluation information by role |
| Session association configuration | Bind chatId to associate the current customer's requirement session | Ensures context consistency across multi-turn dialogue, avoids confusion across customer sessions |
| Recall upper limit setting | Top 5 customer feedback records | Controls the scope of information during duplicate requirement merging, avoids information overload |
Three Common Misconfigurations
- Symptom: No parsed results are returned after uploading requirement-related documents, and no errors are displayed in the backend. Cause: Parsing rules for the corresponding document type are not configured, or file upload field parameters are not correctly bound to
variables. - Symptom: Results from all AI dialogue nodes in the workflow are displayed. Cause: Only retaining the last round of output from
messagesis not set in the node configuration, or context filtering rules are not bound. - Symptom: Stop commands cannot be triggered during multi-turn dialogue. Cause: Trigger rules for stop commands are not configured in the prompt template, or
chatIdis not correctly associated with the current dialogue session.
How to Verify Successful Configuration
- Initiate a test dialogue, enter a query related to the requirement pool, confirm that the context window includes recent requirement records and dialogue history.
- Upload requirement documents, check if parsed results match preset field extraction rules.
- Send a stop command after triggering multi-turn dialogue, confirm that the session terminates normally with no subsequent output.
- Run a workflow containing multiple nodes, confirm that only the output from the last AI dialogue node is displayed.
The values provided are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.