What the data for this category looks like
Text annotation project data primarily comes from customer-provided corpora, annotation results from internal annotation platforms, industry-standard glossaries, and quality inspection sampling results. Daily associated documents include annotation specifications, sample packages, quality inspection reports, and issue feedback forms. Data update frequency is adjusted daily or per batch annotation task cycle. Data fields include text content, annotation tags, annotator, quality inspection results, issue remarks, and more. Core focus areas are annotation information related to semantic boundaries, entities, and intent, which differs from image annotation projects that focus on visual feature dimensions.
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
Diverse data sources updated daily or per batch require that multi-turn dialogue contexts dynamically load the latest annotation specifications and quality inspection results. Non-standardized daily document structures require prompts to adapt to extraction and verification logic for different fields. Core focus on semantic boundaries and entity information requires clear alignment of tag definitions in multi-turn dialogue to avoid ambiguity. In code change review scenarios, historical annotation issue records must be included as part of the dialogue context to assist with compliance review of current changes. Additionally, batch-processed annotation samples require prompts to support logic for processing multiple sets of data in a single request, adapting to the project’s daily workflow rhythm.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier per annotation task batch, formatted as Task ID_Session Sequence | Ensure dialogue history from the same batch of annotations is associated with a unified context, avoiding cross-task confusion |
messages | Retain the most recent 20 dialogue messages, calibrated via actual testing | Adapt to the batch data processing rhythm of text annotation projects, avoiding context overflow |
variables | Pass the current annotation sample, latest annotation specifications, and quality inspection sampling results; one set of variables per sample | Treat category-specific data as dynamic variables in prompts to improve review accuracy |
| Maximum tokens per single dialogue | 8000 token | Cover complete information for a single batch of annotation samples and historical context |
| Prompt template update frequency | Sync once daily | Match the daily or per-batch data update rhythm, ensuring prompts use the latest annotation rules |
Three Common Configuration Errors
- Symptom: After configuring
chatIdas the task ID, no corresponding record appears in the session storage module. Cause: ThechatIdwas not bound to the unique identifier of the annotation task, resulting in a mismatch between session storage and the task. - Symptom: Annotation specification documents can be retrieved during workflow debugging, but no relevant reference content is returned during AI dialogue. Cause: Knowledge base retrieval results were not passed to the dialogue context via
variables, preventing the prompt from accessing reference information. - Symptom: Output results from the program execution module cannot be directly connected to the AI dialogue module. Cause: Program output results were not mapped to custom variables in
variables, preventing the dialogue from reading the execution results.
How to Confirm Configuration Is Complete
- View the session storage module in FastGPT v4.17.0, confirm that a dialogue record corresponding to the
chatIdexists, verifying that the session binding logic is active. - Pass test samples and annotation specification variables, initiate a dialogue, check if the AI’s reply includes relevant content from the annotation specifications, verifying that the
variablesconfiguration is active. - Initiate a continuous questioning test, check if multi-turn dialogue results are integrated as required, verifying that the
messagescontext transfer logic is active. - Adjust the maximum tokens per single dialogue parameter, input an overly long test text, check if the dialogue responds normally, verifying that the context configuration is active.
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.