What data for this category looks like
Data for this category comes from code repositories, test framework reports, requirement descriptions, and interface definitions. It updates daily or after each code commit. Documents produced daily include structured test cases, coverage statistics reports, Mock interface descriptions, and defect tracking sheets. Document fields include test scenario numbers, expected results, code coverage thresholds, Mock response parameters, and more. There is no unified mandatory format. It adjusts based on the team’s development process.
What constraints these characteristics impose on multi-turn dialogue and prompts
Dispersed data sources that update with commits require multi-turn dialogue to associate chatId. This binds the current development session context and avoids repeatedly pulling expired test framework reports. Unstructured document formats require prompts to adapt to multiple field formats. Test case templates and defect sheet field rules from historical dialogue must be passed through messages. High update frequencies require limiting the context length of single dialogue calls. This avoids loading too much old data and causing lag. Dynamic parameters such as the current project’s test coverage thresholds and Mock interface lists must be passed through variables. This ensures generated content aligns with team development standards.
How to set configurations
These configurations apply to FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 token | Adapts to the average length of unit testing and component testing documents, avoids lag caused by loading expired data |
chatId binding rule | Bind chatId of the current development session | Associates context of historical test cases, defect sheets, and coverage reports, avoids session confusion |
messages history transfer limit | First 10 dialogue messages | Filters redundant test discussion content, focuses on current requirement and design document drafting needs |
variables parameter injection | Inject current project’s test coverage thresholds and Mock interface lists | Ensures generated test documents align with team development standards |
| Single dialogue timeout | 600 seconds | Avoids dialogue interruption caused by loading large volumes of test framework reports |
| Question optimization trigger | Calibrated through actual testing | Adapts to different teams’ test document drafting habits, optimizes multi-turn dialogue questioning logic |
Three Common Mistakes
- The symptom is lag during multi-turn dialogue, with logs showing context load timeout. The cause is failure to limit maximum context length, leading to loading too many expired test framework reports and coverage data.
- The symptom is uploaded test cases or Mock documents not being called by the dialogue, with generated content not including field information from uploaded data. The cause is failure to bind uploaded data to the current session context through
variablesormessages. - The symptom is a 400 invalid image error returned when calling the multimodal interface to generate test cases. The cause is incorrect formatting of uploaded interface definition screenshots, failing to match the platform’s required input format.
How to Confirm Configurations Are Properly Set
- Initiate a dialogue for test case drafting. Check if the context includes recently submitted test framework report data, to confirm
chatIdbinding is active. - Upload a Mock interface description document. Initiate a statistical summary request, confirm generated content includes field information from the uploaded document, to verify
variablesparameter injection is active. - Initiate a multi-turn dialogue. Check for no obvious lag during the conversation, to confirm maximum context length and timeout settings are reasonable.
- Call the API interface with a test case screenshot. Check that returned results have no format errors, to verify multimodal input configuration is correct.
All provided configuration values are common starting points. They 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.