What the data for this category looks like
Core data for this category comes from requirement research notes, PRD documents, user story lists, and requirement change forms. Update frequency is weekly or per release cycle. Data fields include business objectives, acceptance criteria, associated test points, change records, and more. The overall structure centers on requirement nodes, with multiple rounds of interview records and version iteration traces. Data sources cover customer interview records, ticketing systems, public competitor pages, and team tracking dashboards. No mandatory standardized format applies, and some documents include Chinese-English bilingual content.
What constraints do these characteristics impose on model integration and configuration
Data characteristics for this category directly constrain model integration and configuration: First, the data has no mandatory standardized format and includes Chinese-English bilingual scenarios. This requires configuring multilingual context processing logic during model integration, to avoid mismatches between test cases and PRD acceptance criteria caused by field recognition errors. Second, the update frequency is weekly or per release cycle. This requires the context window to retain complete version iteration information, to avoid losing old requirement change records due to context truncation. Third, the most time-consuming links of classifying interview information and confirming boundary conditions require model calls to balance rigor and flexibility, balancing the accuracy of requirement analysis and the efficiency of test case generation.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches weekly or per-release requirement document update volume, retains complete PRD, user story list, and change record context to avoid truncation of critical information |
temperature | 0.3–0.5 | Balances the rigor of requirement analysis and the flexibility of test case generation, supports accurate matching of acceptance criteria, and reduces deviations in boundary condition confirmation |
quoteMaxToken | 1500–2000 token | Retains core business objective and acceptance criterion fields from PRD documents, ensures the model can reference key information when generating test cases, and aligns with the core logic of requirement documents |
toolChoice | auto | Automatically triggers tool calls for requirement change verification and test case association, adapts to the synchronization requirements of requirement change forms, and ensures test cases remain consistent with the latest requirements |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions. Do not configure as a single-model service address | Required environment variable for the App/Admin server, used to specify the AI Proxy service address, ensures correct model call routing, and prevents call failures across regions or environments |
AIPROXY_API_TOKEN | Matches the administrator token configured for the AI Proxy server and is non-empty | Required environment variable for the App/Admin server, matches the administrator token configured for the AI Proxy server to secure access to the AI Proxy service and prevent unauthorized access |
Three common configuration mistakes
- Phenomenon: The model prompts that no knowledge base content is obtained, or the generated test cases do not associate with the acceptance criteria in the PRD. Cause: The
maxContextvalue is too small, so the complete PRD and requirement change records cannot be read by the model, and core information is lost due to context truncation. - Phenomenon: The rerank model does not sort PRD content related to test cases as expected. Cause:
quoteMaxTokenis not configured correctly, so the model cannot identify PRD fields that need priority matching. The input context for the rerank model is incomplete, making it impossible to accurately associate test cases with requirement documents. - Phenomenon: Abnormal streaming behavior occurs during Qwen3 model calls, leading to interruption of node model execution. Cause:
maxTokensis not adjusted according to model characteristics, exceeding the non-streaming call context limit supported by the model, triggering call failure.
How to confirm correct configuration
- Check the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENenvironment variables configured on the App/Admin server. Confirm that service startup verification passes, model call routing is correct, and no startup failure prompts are displayed. - Trigger a model call for requirement change synchronization. Confirm that
toolChoiceautomatically triggers the associated test case verification node, no error messages are returned, and test cases are synchronized with the latest requirement change content. - Import a PRD document with Chinese-English bilingual content, generate a set of test cases, confirm that the model matches both Chinese requirements and corresponding English translations, with no content omissions or matching deviations.
- Adjust
temperatureto 0.4, generate 3 sets of test cases, compare the consistency of results, confirm that the value meets expectations, with no overly divergent or overly rigid generation results.
The values provided here are common starting points, and should be evaluated against the user'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.