What data does this use case rely on?
Data for this use case comes from requirement research notes, PRD documents, user story lists, and requirement change forms. Data sources include customer interview records, ticketing systems, competitor public pages, and event tracking dashboards. Data updates follow a weekly or per-version release cycle. Document structures typically include fields such as requirement ID, business goal, acceptance criteria, and change description. There are no fixed mandatory units. Fields are adjusted based on team business rules.
What constraints do these characteristics impose on multi-turn dialogue and prompt design?
Documents for this use case are non-standard and updated frequently. Multi-turn dialogue must gradually align vague business descriptions and acceptance criteria to avoid rework caused by information deviation. Data sources are scattered. Prompts must adapt to input content in multiple formats, including research notes, PRDs, and change forms. Context window size must be limited to prevent redundant old data from interfering with current conversations. The weekly or version-level update rhythm requires the dialogue process to automatically associate the latest requirement change forms. This ensures the information used in interactions stays up to date.
How to configure settings
This setup adapts to the configuration logic of FastGPT v4.17.0.
| Config Item | Recommended Value | Rationale |
|---|---|---|
| Dialogue context retention duration | 7200 seconds | Matches the weekly or version-level requirement update cycle, avoids interference from expired data |
| Maximum number of messages per dialogue | First 10 messages | Adapts to the single-round information density of PRDs and change forms, prevents context overflow |
| Variable binding rules | Associate with current requirement ID | Extracts fields from requirement documents, ensures dialogue context binds to the correct business object |
| Prompt template update frequency | Per version release | Matches the update rhythm of business documents, syncs the latest acceptance criteria requirements |
| Context truncation threshold | 8000 characters | Adapts to the typical length of a single PRD, avoids excessive resource usage from long content |
Three common configuration errors
- Workflow execution terminates directly at the AI dialogue step, with no error prompt. The cause is that the variable parameter for the current requirement ID is not bound. Context cannot associate with business documents, and the process interrupts after triggering verification failure.
- Dialogue logs disappear automatically after the session ends. The cause is that the correct
chatIdparameter is not set. Session context cannot be persisted, and logs are automatically cleaned up. - No streaming return content after calling the API for dialogue. The cause is that the incremental update logic for the
messagesparameter is not configured. Returned content is not pushed according to segment rules.
How to verify correct configuration
- Import a latest PRD document, start a multi-turn dialogue, and verify that dialogue content associates with acceptance criteria fields in the document.
- Start two dialogues for the same requirement, and check that context from both sessions retains historical interaction content.
- Call the API to start a dialogue, and verify that returned content is pushed according to segment rules if streaming output is configured.
- View variable binding items on the configuration page, and confirm that business fields for the current requirement are associated.
The values provided are common starting points and should be measured against applicable 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.