What the data for this category looks like
This category’s data primarily comes from cloud audit logs, IAM permission systems, CMDB resource inventories, and security baseline scan results. Update cycles are monthly or quarterly. Daily produced documents include compliance checklists, permission audit reports, resource inventories, and rectification tracking sheets. Document fields include resource identifiers, permission configuration items, compliance verification results, rectification progress, and more. There are no unified fixed units; values vary based on specific resource types and compliance items.
What constraints do these characteristics impose on the multi-turn dialogue and prompts link
The scattered data sources and slow update rhythm require that multi-turn dialogue prompts clearly distinguish fields and usage scenarios of different data sources, to avoid confusion between cloud audit logs, IAM permission data, and CMDB resource information. There are diverse daily document types, so prompts must specify the call priority for different documents. For example, prioritize calling abnormal items from permission audit reports, while referencing results from single security baseline scans. Additionally, since the consequences of non-compliance audit errors are clear, multi-turn dialogue must retain historical audit scope and compliance item configurations in variables to ensure context consistency, avoiding repeated verification or missed critical items. Also, reference to data outside the current update cycle must be restricted in dialogue to prevent use of expired information.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | First 8000–12000 tokens | Compliance audit-related documents include multiple reports and resource inventories. A longer context can fully retain historical verification scope and abnormal items |
| Historical variable loading configuration | Enabled and bound to variables | Global variables such as previously saved audit scope and compliance checklist version need to be reused in new conversations to ensure verification consistency |
| Multi-turn context splicing rule | Spliced in messages order | User compliance audit requirements and AI verification results must be retained in dialogue chronological order to avoid context confusion |
| Prompt template configuration | Fixed to include data source description | Clearly prompt for calling fields from sources such as cloud audit logs and IAM data to avoid confusion between different types of compliance data |
| Dialogue timeout threshold | 600 seconds | Compliance audit data integration requires traversing multiple documents. A longer timeout can prevent dialogue failure due to data loading interruptions |
Three common configuration errors
- Phenomenon: When calling historical global variables in a new conversation, the variables are empty or not loaded. Cause: Variable binding rules are not configured in workflow nodes, or the
chatIdparameter of the historical session is not specified, making it impossible to associate previously saved audit scope configurations. - Phenomenon: Adding a database tool call returns a 400 status code (no body). Cause: The prompt does not clearly specify the compliance data range for database queries, or does not restrict query fields to permission and resource configuration items required for compliance audits, resulting in invalid requests being blocked.
- Phenomenon: Inconsistent compliance verification results appear in multi-turn dialogue. Cause:
messagescontext is not spliced in dialogue chronological order, causing the AI to confuse audit requirements and verification rules from different rounds, leading to repeated verification or missed abnormal items.
How to confirm successful configuration
- Initiate a new compliance audit conversation, enter audit scope keywords used in historical sessions, and verify that previously saved global variables can be loaded correctly.
- After configuring the database tool call, execute a test query to confirm that returned results include resource and permission fields required for compliance audits, with no 400 status code errors.
- Initiate multiple consecutive conversations, gradually add different compliance verification requirements, and confirm that the AI retains context in dialogue chronological order without repeating or omitting verification items.
- Review dialogue logs to confirm that both the
messagesandvariablesparameters are correctly transmitted, with no missing or formatting errors.
The values provided are common starting points and should be evaluated against samples specific to the actual deployment scenario.
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.