What data looks like for this category
Daily documentation for prototyping and interaction design typically includes interactive prototypes, page flowcharts, component descriptions, and design review records. Data sources primarily include Figma or Axure files, internal team design systems, user feedback, and usability test records. Update frequency is usually once per week or once before each version release. Document structure centers around page operation paths and component interactions, with no fixed standardized fields; content is dynamically adjusted alongside design iterations.
What constraints do these characteristics impose on multi-turn dialogue and prompt engineering
Because data sources are scattered and structures are inconsistent, multi-turn dialogue must use chatId to bind the context of the current prototyping project, ensuring each Q&A can link to all design documents for the corresponding project. Because update frequency is high, prompts must automatically adapt to the latest version of prototype content to avoid referencing outdated design rules. Because documents include numerous exception scenarios and review modification records, multi-turn dialogue must retain sufficient historical messages content to support retrospective review of prior Q&A and modification opinions.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the current prototyping project, formatted as proj_${Figma file ID}_v${version number}, with a validity period of 7 days | Ensure multi-turn dialogue context links to all design documents for the corresponding prototyping project; FastGPT v4.17.0 supports this binding rule |
messages | Retain the first 20 historical conversation entries | Cover complete review and Q&A workflows, preventing loss of critical context |
variables | Pass component descriptions and page flowchart text for the current prototype, truncating to the first 1500 characters | Enable the AI to accurately understand the current design’s operation paths and component rules |
| Model switching settings | Allow switching between the first 3 preset AI models during dialogue | Adapt to prototype analysis and Q&A requirements of varying complexity |
| Authorization verification configuration | Bind the API key for the current project, with a validity period matching the project iteration cycle | Ensure only authorized users can access Q&A conversations for the corresponding prototype |
Three common configuration mistakes
- Phenomenon: When calling the ollama model, preset guiding prompts have no effect, and the AI does not generate Q&A content according to the design scenario. Cause: The
variablesconfiguration is not bound to the prototype’s scenario variables, causing the prompt to fail to retrieve core rules from the design system. - Phenomenon: AI models cannot be switched in the chat dialog box, and conversations do not respond after switching. Cause: Context parameters for model switching are not included in
messages, causing the system to fail to recognize the switch request. - Phenomenon: An unauthorized access prompt appears even after using a knowledge base API key, and conversations cannot be initiated. Cause: The API key is not linked to the
chatIdcontext of the corresponding prototyping project, so the system cannot match the authorization scope.
How to verify successful configuration
- Upload a test Axure or Figma prototype file, send the first Q&A request, and check if the system can correctly extract and associate basic prototype information.
- Send multiple follow-up questions, and check if the system retains historical conversation context and the project identifier bound by
chatIddoes not change. - Attempt to switch preset AI models, and check if conversations can normally respond to output from the new model.
- Enter an incorrect API key, and check if the system triggers an unauthorized prompt to verify that the authorization configuration is active.
The values given 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.