What the data for this category looks like
Data sources for brand agency operation intelligent due diligence reports include internal operation ledgers of agency institutions, authorized business data of cooperating brands, public placement records from third-party content platforms, and interaction data exported from public opinion monitoring tools. Data updates are executed according to service cycles. Core operational performance data is updated monthly, and full due diligence documents are updated quarterly.
The standard document is divided into four modules: qualification verification, operational performance, compliance review, and public opinion feedback.
- The qualification verification module includes institution establishment years (unit: year) and service qualification level
- The operational performance module includes monthly content release volume (unit: posts), platform placement budget (unit: ten thousand yuan), and user interaction volume (unit: times)
- The compliance review module includes number of compliant items (unit: items) and total number of review items (unit: items)
- The public opinion feedback module includes monthly negative mentions (unit: posts)
What constraints these characteristics impose on the "multi-turn dialogue and prompt engineering" link
The scattered data sources and modular structure of brand agency operation due diligence reports require multi-turn dialogue to guide data verification in the order of qualification verification, operational performance, compliance review, and public opinion feedback, to avoid missing fields. The monthly and quarterly update rhythm requires prompt engineering to clearly limit the use of only the latest data from the current service cycle, excluding expired historical records. The exclusive units of different fields require prompt engineering to forcibly match the corresponding units during interaction, to avoid unit confusion. The need to retrieve data from multiple sources requires dialogue context to retain independent identifiers for each module, to facilitate accurate association of corresponding data during subsequent integration.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Multi-turn dialogue for agency due diligence reports needs to retain multi-module data sources and interaction records. This window size covers the complete module interaction flow |
prompt_template | Guide verification in the order of qualification verification → operational performance → compliance review → public opinion feedback, clearly specify the unit of each field, and limit the use of only the latest data from the current service cycle | Matches the modular structure and unit constraints of agency due diligence reports. Guiding in order avoids interaction confusion |
recall_top_k | Top 6 entries | The due diligence report data source includes 4 core modules, with 2 additional entries reserved for supplementary context association to ensure complete data coverage |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Agency due diligence reports include multiple ledgers and reports, which take a long time to parse. 300 seconds covers the parsing needs of most documents |
UPLOAD_FILE_MAX_SIZE | 500 MB | Agency due diligence reports include multiple platform placement reports and public opinion data, with large individual document sizes. This value meets conventional upload requirements |
enable_multi_round_confirm | Enabled | Agency due diligence reports involve many fields. Multi-round confirmation avoids missing key data and improves report accuracy |
The parameter values provided on this page are common starting points for configuration. The actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the settings.
Three common mistakes
- Phenomenon: The prompt configuration does not take effect, and the generated due diligence report does not organize data in the specified order. Cause: The prompt template is not bound to the corresponding dialogue node, or the template does not clearly specify the exclusive field requirements for agency due diligence reports.
- Phenomenon: The front-end dialogue page displays output content from multiple modules at the same time, and it is impossible to restrict the content of a specified module to an independent area. Cause: The module division rules of the dialogue interface are not configured, and the exclusive container for user input and AI output is not specified via parameters.
- Phenomenon: Docker-deployed instances cannot initiate chats normally, and the dialogue process remains in a loading state. Cause: Port mapping of environment variables is not correctly configured during deployment, or the service process inside the container fails to start normally.
How to confirm the configuration is complete
- Open the dialogue configuration page, check whether the value of
maxContextmeets the interaction length requirements of the current business, and adjust by referring to the number of modules and interaction rounds of the agency due diligence report. - Upload a sample agency due diligence document, trigger the parsing process, and confirm that the parsing time meets the value requirements of the configured
PARSE_FILE_TIMEOUT_SECONDS. - Initiate a simulated dialogue, verify data according to the guidance order of the prompt template, and confirm that the AI output content strictly matches the specified field units and module order.
- Enter the released test environment, initiate a dialogue and input compliance data in LaTeX format, and confirm that the interface can normally render LaTeX content.
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.