What Data for This Category Includes
Investment platform marketing content data originates from platform-owned user holdings, product operation databases, public regulatory disclosure documents, and user interaction logs. Update frequencies differ across data types: Product net value data updates daily after market close. Industry research report data updates at its publication time. User interaction logs are generated in real time.
A single product document typically contains fields including product code, full name, risk level, performance benchmark, establishment date, unit net value, and cumulative net value. Net value fields use yuan as their unit. Yield fields use percentage as their unit. Overall document length varies significantly. It is recommended to conduct statistics or testing using internal samples before finalizing values.
Constraints on Multi-turn Dialogue and Prompt Engineering
Differences in data update rhythms, field standardization requirements, and compliance attributes impose multiple constraints on multi-turn dialogue and prompt configuration for investment platforms.
First, product net value data updates daily. Multi-turn dialogue context must limit its valid time range to avoid generating marketing content using expired data.
Second, product document fields are numerous and have clear units. Prompts must precisely specify required extracted fields and formats to prevent unit confusion or missing fields in generated content.
Third, public regulatory disclosure data must strictly follow compliance requirements. Prompts must include built-in compliance check logic to ensure generated content aligns with industry regulatory rules.
Fourth, the real-time nature of user interaction logs requires multi-turn dialogue to process context quickly, avoiding delays that impact marketing content generation efficiency.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the average length of investment platform product documents and interaction logs, prevents generation interruptions caused by context overflow |
contextExpireTime | 24 hours | Matches the daily update rhythm of product net value data, avoids generating marketing content using expired data |
systemPrompt | For investment marketing scenarios, explicitly specify required product fields, compliance check rules, and context valid range | Aligns with the compliance attributes and field standardization requirements of investment platform data, ensures generated content is accurate and compliant |
httpRequestTimeout | 30 seconds | Adapts to the loading latency of public research reports and regulatory data, prevents workflow interruptions caused by timeouts |
filterInvalidContext | Enabled | Filters redundant content in user interaction logs, improves multi-turn dialogue processing efficiency |
maxRetryTimes | 2 times | Balances request success rate and resource usage, avoids additional overhead from repeated requests |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on internal samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Workflow output includes both the previous AI conversation reply and the final generated marketing copy. Cause: The output of the previous conversation is retained in the global conversation history instead of being used only as an input parameter for the second conversation, leading to repeated context injection.
- Phenomenon: A workflow is configured with an HTTP request for online search, but the request is not triggered during execution. The AI uses its built-in knowledge base to generate content directly. Cause: The trigger node for the HTTP request is not set in the workflow, or user questions are not correctly bound to request parameters.
- Phenomenon: The frontend calling the
/api/v1/chat/completionsinterface returns a cross-origin error prompt with status code 403 orNo 'Access-Control-Allow-Origin' header is present on the requested resource. Cause: Allowed frontend access domain names are not configured on the platform backend, or cross-domain response headers are not added correctly.
How to Verify Correct Configuration
- Initiate a test dialogue, enter a query that includes product net value and risk level, verify that the generated content uses the latest field data and contains no expired information.
- Configure a workflow that includes an HTTP request, trigger a test, check the workflow logs to confirm the request node has executed and returned valid data.
- Check the request headers of frontend interface calls and the cross-domain whitelist configured on the backend, confirm that domain names match and header information is correct.
- Initiate multiple consecutive dialogues, verify that conversation history is filtered according to the configured expiration time and context length, with no redundant content remaining.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.