What the data for this category looks like
Data for this category originates from content moderation backends, user reporting channels, public regulatory policies, and internal sample libraries. Daily produced documents include moderation rules, disposal guidelines, appeal records, and quality inspection reports. Document fields include rule identifiers, trigger conditions, matching samples, appeal status, and additional relevant fields. Data updates occur either daily or when triggered by policy and regulatory changes. The number of rule entries and sample volume covered in a single update adjusts based on business requirements.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering workflow
The data sources for this category are scattered, and update schedules are inconsistent. This requires multi-turn dialogue to retain context associated with the latest moderation rules and sample data. Different types of business documents, such as disposal guidelines and appeal records, require clear call boundaries defined via prompt engineering to avoid confusion between rule clauses and appeal cases. Data updates are triggered by policy changes, so a dynamic reference mechanism must be configured to ensure that knowledge base content called during each dialogue is the currently active version. Additionally, moderation results directly relate to compliance risks. Multi-turn dialogue must support step-by-step verification of referenced content accuracy to prevent misuse of expired rules or incorrect samples.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 8000–12000 characters | Supports context association for long texts such as moderation rules and appeal records |
variables | Pass the current moderation scenario ID and rule base version number | Dynamically loads the active rule subset for the corresponding scenario, avoiding calls to expired content |
messages | Split system prompts and user conversations by business phase | Clearly distinguishes different prompt logic for rule explanation and sample verification |
| Conversation history retention rounds | First 3 full conversations + latest 5 rounds of context | Covers the complete business process of rule explanation and sample judgment |
| Knowledge base reference limit | Top 6 matching pieces of content | Avoids overload, focusing on highly relevant moderation rules and cases |
chatId | Bind to a single moderation task ID | Ensures multi-turn dialogue context is only associated with current task data, avoiding cross-task confusion |
Three Common Misconfigurations
- Phenomenon: After configuring the prompt, calling the knowledge base returns content that does not match the preset moderation rules. Cause: Failed to distinguish configuration logic between prompt templates and reference templates, incorrectly mixing reference rules into prompt variables.
- Phenomenon: Online dialogue returns accurate moderation rule explanations, but API calls return significantly different results. Cause: API calls did not correctly pass the
variablesparameter to bind the current moderation scenario, and themessagesparameter structure was not standardized, leading to failed scenario matching. - Phenomenon: After submitting batch moderation tasks, some dialogue requests time out or are rejected. Cause: Concurrency configuration was not adjusted based on the current FastGPT version, and default parameters cannot support the request volume for batch scenarios.
How to Verify Successful Configuration
- Manually trigger a single-turn dialogue, check if returned moderation rules are the currently active version, and verify that the
variablesparameter passes the correct scenario ID. - Compare return results of online dialogue and API calls, confirm outputs are consistent when the same
messagesandvariablesparameters are passed. - Test multi-turn dialogue, check if context retains previous moderation rule references, and no context overflow or loss occurs.
- Trigger the AI reply step in the workflow, confirm that annotation steps only execute after the AI reply is complete, and do not trigger early.
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.