What data for this category looks like
Typical subjects of this category include product designers, interaction designers, and front-end product teams. Daily output documents include interactive prototypes, page flowcharts, component descriptions, and review records. Data mainly comes from Figma or Axure files, internal team design systems, user feedback, and usability test records. Update frequency is usually weekly or per version iteration. Document structure takes page operation paths, component attributes, and state transitions as core fields. There are no unified mandatory unit standards. Dimensions, interaction logic descriptions, and similar content are only labeled per design scenario.
What constraints do these characteristics impose on context and token processing
Documents in this category focus on page interaction details and component attributes. Single pieces of content have wide scope and are mostly non-code text. Context and token processing must avoid losing the associated logic of page-level operation paths after splitting. Frequent weekly or per-version updates require context synchronization mechanisms to adapt to incremental changes, avoiding repeated processing of full documents. At the same time, properly set maxContext and quoteMaxToken parameters to adapt to the non-standard text structure of design documents. Ensure that context associated with reviews is not truncated, and filter low-relevance historical records.
How to set the configuration
This configuration guide applies to FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Adapts to the single-page content length of interactive prototypes and review records, avoids truncating core operation path descriptions |
quoteMaxToken | 600–800 tokens | Adapts to the detail density of component descriptions and page flowcharts, ensures complete context associated with reviews |
| Context recall count | Top 3–5 entries | Matches the range of associated documents used in daily designer reviews, reduces irrelevant content occupying token quota |
| Context matching threshold | Calibrated via actual testing | Adapts to the non-standard text structure of design documents, filters low-relevance historical review records |
| Single update processing volume | 1–2 version segments | Adapts to weekly or per-version update frequencies, avoids excessive token consumption in a single processing |
Three common mistakes
- Phenomenon: After splitting interactive prototypes, the context association of operation paths between adjacent pages fails, leading to logical gaps during reviews. Cause: Context recall rules are not configured according to page hierarchy, and splitting is only done per single text segment, resulting in lost cross-page associated logic in split fragments.
- Phenomenon: During change reviews, historical modification records are not correctly associated with the current prototype segment, and classification results tend to fall back to default categories. Cause: The context synchronization mechanism is not enabled, and matching is only based on the current segment, without introducing associated data from historical reviews.
- Phenomenon: When processing large documents exported from Axure, token overflow errors occur and single processing takes too long. Cause: The upper limit of
maxContextis not restricted, causing a single large prototype file to occupy excessive token quota.
How to confirm the configuration is correct
- Import a typical interactive prototype and review records, check if split fragments retain page-level operation path associations.
- Submit a change review request, verify that the recalled context includes historical records and component descriptions related to the current review.
- Check token consumption statistics, confirm that the token usage for a single processing falls within the platform’s normal range.
- Simulate a high-frequency update scenario, check if the context can synchronize the latest modified content.
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.