What data for this category looks like
Data for this category primarily comes from enterprise databases, data dictionaries, in-house masking tools, and access approval systems. Updates align with each batch of data processing tasks or individual project progress. Daily produced documents include masking plans, field mapping tables, masking validation reports, and approval records. Field mapping tables must link original fields to masking rules. Masking validation reports must record sensitive field identification results and the availability of masked data. Core fields include sensitive field identifiers, masking rule types, approval status, and similar attributes. There are no uniform fixed units of measurement.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link
Decentralized data sources require multi-turn dialogue to sequentially call different systems to obtain information such as field mappings and approval records. The variables parameter must be used to pass batch project identifiers to locate corresponding data. Update frequency varies by project, so prompts must adapt to the context scope of a single task to avoid mixing cross-project data. Daily documents have complex structures, so multi-turn dialogue must split prompts by document type, guiding sensitive field identification and rule configuration validation separately. The uniqueness of sensitive fields requires dialogue context to retain the correspondence between field identifiers and masking rules, preventing configuration errors caused by context overflow.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId binding | Bind the current project session ID | Isolate dialogue contexts for different batches of masking processing to avoid data confusion |
messages pruning rule | Only retain 2–3 rounds of dialogue for the current task | Filter redundant information from cross-project documents to reduce context interference |
variables parameter passing | Pass project batches and sensitive field ranges | Locate data sources such as the current task's database and field mapping tables |
| Maximum context length | 1800–2200 characters | Adapt to the total length of multiple documents including masking plans and validation reports |
| Timeout threshold | 600 seconds | Adapt to response times for obtaining masking tools and approval records across systems |
| Prompt template splitting | Split by masking stage | Correspond to sensitive field identification, rule configuration, and availability validation stages respectively |
Three common configuration errors
- Symptom: The workflow displays all outputs from multi-turn AI conversations after running. Cause: No dialogue history pruning rule is configured, so all past dialogue contexts are retained.
- Symptom: When accessing via external channels, dialogue results carry a large number of non-essential punctuation marks such as # and *. Cause: No requirement to remove non-standard format markers is specified in the prompt.
- Symptom: The associated project data source identifier is not returned when calling the dialogue interface. Cause: The current project's batch parameter is not passed via
variables, causing the model to bind to an incorrect data source.
How to verify successful configuration
- Trigger a masking review dialogue for a test project, check that the workflow only displays the final round of AI output, confirming that the dialogue history pruning configuration is effective.
- Call the dialogue interface, check that the returned results include the current project's batch identifier, confirming that the
variablesparameter passing configuration is correct. - View the multi-turn dialogue context logs, confirm that only the specified rounds of dialogue for the current task are retained, confirming that the
messagespruning rule is effective. - Manually trigger the sensitive field identification stage, check that the fields output by the model match the current project's field mapping table, confirming that the prompt template splitting configuration is effective.
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.