What the data for this category looks like
This category’s data primarily comes from business databases, data dictionaries, masking execution tools, and access approval systems. Updates occur per batch project or single task. Daily associated documents include masking solution documents, sensitive field mapping tables, masking effect verification reports, and access approval records. Data fields include sensitive field identifiers, masking rule types, execution status, approval node information, and so on. No unified fixed units exist. Values adjust based on project configuration. Data security engineers, data service teams, and compliance personnel maintain the data. The data supports core processes for sensitive information identification and processing.
Constraints imposed by these characteristics on multi-turn dialogue and prompt words
This category’s data updates in batches per project and links to multiple types of structured documents. Multi-turn dialogue must bind project-level context to avoid cross-project data confusion. Sensitive fields and rules change dynamically. Prompt words must support passing project-specific variables to adapt to field mapping rules for different batches. Compliance rules restrict the valid scope of multi-turn dialogue context storage. This prevents sensitive information from leaking via conversation history. Masking verification reports and approval records use non-standardized formats. Prompt words must explicitly specify the document type and fields to retrieve. This reduces interference from invalid information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum context length | First 10 turns of dialogue or 800–1200 characters | This category’s data updates per project. Excessively long context may introduce cross-project sensitive information. Limiting length reduces compliance risks |
| Automatic context clearing condition | Triggered by project switch | This category’s data updates per batch or project. Clearing history when switching projects prevents confusion between masking rules of different projects |
| Variable passing rule | Pass variables of the current project and field mapping table ID | This category’s documents include exclusive field mapping tables. Passing corresponding variables lets the model accurately call the current project’s masking configuration |
| Dialogue identifier binding method | Bind chatId and project ID | This ensures multi-turn dialogue context only associates with the current project. It prevents cross-session data leaks |
| Prompt template priority | Prioritize project-specific masking prompt templates | Masking rules for this category vary widely by project. Exclusive templates improve prompt adaptability |
Three common configuration mistakes
- Phenomenon: After consecutive questions about masking rules in multi-turn dialogue, the model returns field mapping information with cross-project confusion. Cause: Project-level dialogue context is not bound. Historical dialogue mixes in masking data from other projects.
- Phenomenon: After enabling multi-turn dialogue, answer accuracy for the same question drops as dialogue turns increase. Accuracy returns to normal after opening a new dialogue. Cause: Context storage length is not limited. Excessively long historical dialogue introduces irrelevant masking verification report content. This interferes with model judgment.
- Phenomenon: When configuring context clearing logic in a workflow, the clearing condition cannot trigger accurately. Cause: The trigger uses fixed turn count or time threshold instead of project switch. This does not match the project-based data update characteristics of this category.
How to confirm the configuration is complete
This document is written based on FastGPT v4.17.0.
- Initiate a cross-project test dialogue. Check that the model returns masking rules only associated with the current test project’s configuration. No sensitive field information from other projects should appear.
- Conduct multiple consecutive questions related to masking rules. Verify that answer accuracy does not decrease without cause. Adjust the maximum context length threshold according to project compliance requirements.
- Test switching projects. Check that historical dialogue automatically clears. Confirm the new dialogue only uses documents and passed variables for the current project.
- Generate a dialogue sharing link. Verify that access requires corresponding project authentication configuration. This ensures dialogue content with sensitive data is not accessed by unauthorized users.
The configuration values provided are common starting points and should be evaluated against samples relevant to the specific use case.
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.