What this category of data looks like
This category of data primarily originates from crowdsourcing platform backends, annotation quality spot check records, annotator training exam results, and financial settlement systems. It is linked to code change review task IDs. Daily maintained documents include annotator access rules, onboarding training materials, monthly performance ranking tables, settlement detail lists, and code review specifications. Data update cycles are split into daily and monthly updates:
- Annotation quality data, same-day settlement details, and same-day code change review records are updated daily.
- Annotator access rules and annual performance summaries are updated monthly.
Data fields include annotator ID, access assessment score, annotation accuracy, settlement amount, linked review task ID, and additional relevant fields. These must align with the category’s own human resource organization and code review management logic.
Constraints imposed by these characteristics on multi-turn dialogue and prompting
Data sources are scattered and linked to code review tasks. Multi-turn dialogue must integrate annotator management, quality data, and code change review records. Prompts must clearly distinguish calling logic for different data types. Update frequencies vary widely. Dialogue contexts must support dynamic loading of daily or monthly updated data. This prevents use of expired performance or review information. Document types are diverse. Prompts must adapt to question-and-answer scenarios for different documents such as access rules, training materials, and review specifications. Dialogue access to document ranges must be restricted to prevent confusion between different categories of annotator management and review documents. Subject roles are diverse. Multi-turn dialogue must adapt to different query needs of operations, quality control, and supervisory staff. For example, quality control staff focus on quality data and review results. Supervisors focus on performance settlement and annotator access.
How to Configure
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Multi-turn dialogue history split by role, including system prompts, user queries, and assistant replies | Adapts to multi-turn query logic for code review and annotator management, supports context association |
variables | Binds dedicated fields such as annotator ID, project ID, and code review task ID | Accurately calls business and review data for the corresponding category |
chatId | Binds project-specific session ID | Isolates annotator and review management dialogues across different projects, prevents data confusion |
| Knowledge Base Tag Filtering | Only enable tags for "Annotator Access Rules", "Training Materials", "Performance Tables", "Settlement Lists", and "Review Specifications" | Restricts access scope, prevents interference from irrelevant documents |
| Attachment Upload Limit | Total single upload size no more than 1000 MB, single file no more than 300 MB | Adapts to uploads of training materials, review specifications, settlement lists, and other documents within the category |
| Dialogue History Retention Period | 7 days | Matches the update cycle of monthly performance and review summaries, avoids loading expired data |
This configuration applies to FastGPT v4.17.0.
Three Common Misconfigurations
- Phenomenon: Calling the dialogue interface returns
400 Bad Request, with a prompt indicating thevariablesfield is missing. Cause: Dedicated business fields such as code review task ID or annotator ID are not bound, making accurate calling of corresponding review and annotator management data impossible. - Phenomenon: Uploaded code review specifications cannot be called in subsequent dialogues. Cause: Knowledge base tag filtering is not configured correctly. Uploaded files are not linked to the category-specific tag group.
- Phenomenon: Dialogue context returns expired monthly performance data. Cause: A reasonable dialogue history retention period is not set, leading to loading of data beyond the update cycle.
How to Verify Proper Configuration
- Initiate a query that includes annotator ID and code review task ID. Confirm the assistant returns corresponding personnel’s performance scores, annotation accuracy, and review results.
- Upload code review specifications or training materials within the category. Initiate related questions. Confirm the assistant correctly references the uploaded document content.
- Call the dialogue interface. Check that returned results include correctly populated
messagesandvariablesfields. - Review the dialogue history retention period setting. Confirm conversations older than 7 days are not automatically loaded.
The values provided here are common starting points, and 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.