What the Data for This Category Looks Like
Data for this category originates primarily from ticketing systems, customer interview records, CRM systems, and product roadmaps. Updates follow a weekly or per-version release cycle. Daily produced documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. Core fields include requirement source channel, priority weight, associated customer identifier, and corresponding version scheduling node. Some teams add an additional research and development task ID field linked to requirements. No unified mandatory format exists, and settings adjust based on team management habits.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering Workflows
Decentralized data sources require multi-turn dialogue to integrate context from multiple systems such as ticketing and CRM. The messages parameter must support text concatenation across data sources. Fixed update cadences mean prompts must adapt to weekly or per-version requirement pool refresh logic, to avoid reprocessing archived requirements. Non-standardized document structures require prompts to clearly define field extraction rules, differentiate core fields such as requirement source and priority, and accommodate non-standard requirement evaluation forms and customer feedback records. The lack of unified field formatting means multi-turn dialogue context management must add format validation steps, to ensure requirement information accuracy and avoid mixing data from different sources.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current requirement pool session | Ensure multi-turn dialogue context only associates with the currently processed requirement pool, avoid cross-session confusion. Reference FastGPT v4.17.0 session management specifications |
messages | Retain the last 12 interaction records and 2 latest customer feedback documents | Adapt to daily interaction density of requirement pools, balance context completeness and response speed |
variables | Preset three variables: requirement source, priority, and associated customer | Quickly extract standardized fields, accommodate non-standard requirement document formats |
| Maximum context length | 6000–10000 token | Cover total content volume of multi-turn dialogue and requirement documents, calibrated via actual testing |
| Dialogue trigger timing | Trigger at requirement pool update nodes | Match the weekly or per-version update cadence of this category, reducing invalid calls |
Three Common Configuration Mistakes
- Symptom: Requirement fields returned by multi-turn dialogue are missing or disorganized. Cause: Core requirement pool fields to extract are not clearly specified in the prompt, leading the model to mix content from different documents.
- Symptom: Historical requirement evaluation results from multi-turn dialogue cannot be correctly referenced. Cause: Historical interaction context fragments are not retained in order in the
messagesparameter, preventing the model from tracing prior requirement communication details. - Symptom: Token consumption exceeds expectations after multi-model orchestration. Cause: Independent
chatIdis not assigned to each requirement pool session, leading to context from different sessions being combined for token consumption calculation.
How to Verify Successful Configuration
- Manually initiate a multi-turn dialogue for a requirement pool, check if the returned results include the
chatIdidentifier bound to the current session, confirm context is not shared across sessions. - Import a non-standard requirement evaluation form, verify that the dialogue can correctly extract the preset requirement field variables, confirm prompt rules are active.
- Simulate two consecutive requirement pool update cycles, check that the dialogue only processes requirements from the current cycle, confirm update cadence matches the configuration.
- Reference the FastGPT v4.17.0 session logging documentation, verify that the number of contexts retained by the
messagesparameter matches the preset value.
The values provided are common starting points, and should be evaluated against local deployment 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.