What data looks like for this category
Typical stakeholders for this category are product designers, interaction designers, and front-end product teams. Daily deliverables include interactive prototypes, page flowcharts, component descriptions, and review records. Data mainly comes from Figma or Axure files, team design systems, user feedback, and usability test records. Update frequency is usually weekly or per version iteration. Core content focuses on page structure and operation paths. The most time-consuming steps involve sorting out state flows, completing edge scenarios, and revising design reviews.
Constraints imposed on model access and configuration
The layered nested structure of interactive prototypes and flowcharts requires model context configuration to adapt to the content volume of multiple documents. This avoids review interruptions caused by context overflow. The high update frequency of documents requires that the context window for model calls not be set too large. This prevents redundant historical data from interfering with current review results. The focus of data on page operation paths requires configuration to support visual content parsing. It also requires strict control of output formats to ensure review results comply with design specifications. In addition, errors directly affect R&D implementation. Model configuration must balance security and output consistency to avoid rework caused by abnormal outputs.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the layered content volume of multiple documents such as interactive prototypes and flowcharts, avoiding review interruptions caused by context overflow |
maxTokens | 2000–3000 token | Controls the output length of a single review round, adapts to the granularity requirements of design documents, and prevents redundant content generation |
temperature | 0.2–0.4 | Reduces output randomness, ensures consistency of review results that comply with design specifications, and reduces abnormal format outputs |
AIPROXY_API_ENDPOINT | Fill in the root address of the AI Proxy service, in the format such as http://aiproxy:3000. Do not append paths such as /v1 or /chat/completions. Non-single model address | Ensures correct routing of the App/Admin server, adapts to FastGPT v4.17.0 instances deployed locally or in the cloud |
AIPROXY_API_TOKEN | Fill in the administrator token consistent with the AI Proxy server configuration, and it cannot be empty | Ensures the security of AI Proxy call permissions, and complies with permission control requirements for security and compliance reviews |
Three common configuration errors
- After upgrading the script, simple applications throw uncaught exceptions. The symptom is an uncaught exception popup on the interface. In Ubuntu Docker deployment environments, logs show connection timeout or permission errors. The cause is failure to configure the required
AIPROXY_API_ENDPOINT(AI Proxy service root address) andAIPROXY_API_TOKEN(AI Proxy administrator token) on the App/Admin server, or failure to load the corresponding environment variables. This leads to startup verification failure or inability to connect to the AI Proxy service. - The model setting disables thought output, but think tags still appear. The symptom is that generated content includes text wrapped in
<think>. The cause is failure to settemperaturewithin a reasonable range, or failure to enable strict output format verification in the model configuration. This leads to random output breaking preset rules. - Simple models can be selected in the workflow but cannot be called. The symptom is that only some models appear in the workflow node dropdown menu, or an error occurs after selection. The cause is failure to associate the model with the corresponding data source configuration, or failure to configure data input format adaptation to interactive prototype files in the workflow.
How to confirm successful configuration
- Call the test interface, input a page flowchart text exported from Axure. Check whether the returned result includes review opinions that comply with design specifications, and there are no uncaught exceptions.
- View the App/Admin server logs. Confirm that
AIPROXY_API_ENDPOINT(AI Proxy service root address) andAIPROXY_API_TOKEN(AI Proxy administrator token) have been loaded correctly, and there are no permission errors or routing errors. - Adjust the
temperatureparameter to 0.3. Generate 3 rounds of test outputs, check the format consistency of the results, and confirm that no additional thought tags are output. - Import a complete interactive prototype file. Verify whether the model can correctly identify page structures and operation paths, and the generated review content has no redundancy or deviation.
The values provided are common starting points and should be measured 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.