What data for this category looks like
Text annotation projects draw core data from customer-provided corpora, annotation samples generated by internal annotation platforms, industry standard vocabularies, and quality inspection sampling results. Teams produce daily documents including annotation specifications, sample packages, quality inspection reports, and problem feedback forms. Data updates occur daily or after each batch of annotation tasks completes. Data fields include text annotation-specific content such as entity boundaries, intent tags, and semantic classification. No pixel coordinate fields for image annotation are included. Work centers on semantic understanding and annotation consistency.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Text annotation projects have scattered data sources and high update frequencies. This requires multi-turn dialogue and prompt configurations to adapt to dynamically changing annotation rules and sample content. The diversity of daily documents means prompts must support injection of multiple exclusive fields. Generic templates cannot cover core time-consuming tasks such as label boundary explanations and difficult sample arbitration. High-frequency data updates require dialogue contexts to quickly bind exclusive parameters for the current batch. This prevents confusion of annotation rules across batches. Annotation errors directly lead to model training deviations or delivery delays. Therefore, multi-turn dialogue must include result verification and format constraint capabilities. This ensures output complies with annotation specification requirements.
How to set the configuration
This configuration is based on FastGPT v4.17.0. Specific parameters and recommended settings are as follows:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Dynamically adjusted based on the total character count of annotation specifications and sample packages, 800–2000 characters | Adapts to differences in document length for text annotation projects, avoids context overflow |
variables | Inject the current batch’s annotation rule ID and quality inspection threshold, 1–5 variables | Binds exclusive parameters for annotation projects, ensures prompts adapt to the current task |
chatId | Bind the unique identifier of the current annotation batch, fixed value | Maintains context consistency for dialogue within the same batch, prevents cross-batch confusion |
| Maximum context length | 10000–15000 characters | Meets the need to load content from multiple annotation documents, sample packages, and quality inspection reports |
| Multi-turn dialogue round limit | 3–5 rounds | Focuses on core time-consuming tasks such as label boundary explanations and difficult sample arbitration, avoids redundant interactions |
| Prompt template variables | Calibrated based on actual testing | Adapts to exclusive field requirements for different annotation tasks, such as entity types and intent classification rules |
Three common errors
- Symptom: Multi-turn dialogue outputs an unexpected number of results. Cause: Context resets because
chatIdis not bound, and the context reinitializes for each dialogue turn. - Symptom: Prompts cannot inject annotation rule variables. Cause: The injection order of the
variablesparameter is not configured as required. Exclusive fields fail to load correctly. - Symptom: Consecutive questions with the same input content produce scattered outputs instead of a unified result. Cause: Prompts and input corpora for multi-turn questions are not bound in order in the
messagesparameter. This leads to fragmented results.
How to confirm the configuration is complete
- Check if
chatIdis bound to the unique identifier of the current annotation batch. Ensure dialogue contexts within the same batch are not disrupted by cross-batch operations. - Verify that the
variablesparameter injects exclusive fields such as annotation specifications and quality inspection thresholds. Confirm that prompts can load project-specific rules. - Test input the same difficult sample. Check if multi-turn dialogue focuses on core tasks such as label boundary explanations and consistency sampling inspections.
- Confirm that the multi-turn dialogue round limit is set to a reasonable range. Avoid unnecessary redundant interactions.
The values provided 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.