What the Data for This Category Looks Like
Data sources for brand agency operation yield and market daily reports include the e-commerce store backends of serviced brands, advertising management platforms, and content marketing data mid-platforms. The system updates full data for the previous calendar day each midnight. It supports filtering by service account dimension. Each data document corresponds to a single service account’s daily operation record, including fields such as account ID, service period, total daily revenue, daily advertising cost, daily content impressions, daily in-store conversions, and core category sales proportion. Revenue and cost are measured in yuan, impressions in times, and conversions in person-times.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
Multiple data sources require prompts to clearly specify data verification logic, prioritizing cross-verified data from e-commerce backends and advertising platforms. The daily update rhythm requires the dialogue flow to limit the data time range to the calendar day dimension, and prohibit requesting real-time unupdated data. The document structure split by account requires multi-turn dialogue to first receive the service account ID provided by the user, then call the corresponding dataset. The rich field characteristics require prompts to limit the minimum set of returned fields, avoiding irrelevant output. Additionally, multi-turn dialogue must retain the account and time parameters specified by the user, to support subsequent month-over-month and year-over-year follow-up questions.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Brand agency operation daily reports have many data fields. Multi-turn dialogue needs to retain context such as account, time, and data verification rules. This interval can cover the context requirements of conventional multi-turn dialogues. |
json_schema | {"type": "object", "properties": {"account_id": {"type": "string"}, "date": {"type": "string"}, "revenue": {"type": "number"}, "cost": {"type": "number"}, "conversion": {"type": "number"}}, "required": ["account_id", "date"]} | Matches the core fields of brand agency operation daily reports, standardizes reply formats, and avoids unstructured output. |
recall_top_k | Top 3 entries | Brand agency operation daily report data is aggregated by account, with concentrated data volume per account. Excessive recall increases context redundancy and reduces reply accuracy. |
prompt_template | Please generate a structured daily report based on the agency operation data for {account_id} on {date}, follow the json_schema for the return format, and only include core operating metrics | Clarifies task scope and output format, reduces invalid output, and aligns with user’s daily report broadcasting requirements. |
history_max_turns | Top 5 turns | Multi-turn follow-up questions for brand agency operations usually focus on three dimensions: account, time, and indicator comparison. Excessive turns consume context windows. This value covers conventional follow-up scenarios. |
similarity_threshold | 0.75–0.85 | Brand agency operation data fields have a high degree of standardization. A threshold that is too low introduces irrelevant data, while a threshold that is too high misses matching historical dialogue context. |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the settings.
Three Common Misconfigurations
- The symptom is that the reply does not follow the specified JSON format, with missing fields or unstructured text. The cause is failing to explicitly bind the
json_schemaparameter in theprompt_template, or incomplete configuration of required fields injson_schema. - The symptom is that multi-turn dialogue execution time exceeds expectations, being several times slower than single-step debugging. The cause is failing to limit the value of
recall_top_k. Excessive recall of data causes context window overload and increases model processing time. - The symptom is that when entering a conversation again, the automatic reply content is empty, and the account and time parameters in the historical conversation are lost. The cause is failing to enable conversation history persistence configuration, or setting
history_max_turnsto 0, resulting in no persistent storage of conversation context.
How to Verify Successful Configuration
- Upload sample brand agency operation daily report data, trigger a conversation, and check whether the reply strictly follows the preset
json_schemaformat, and whether the fields include core operating metrics. - Initiate two follow-up inquiries, changing the account and time parameters respectively, and check whether the conversation context is correctly retained, and whether subsequent replies are generated based on the parameters from the previous round.
- Adjust the value of
similarity_threshold, test recall results under different thresholds, and confirm that the recalled data and the relevance to the current task meet expectations. - Check the logs in the conversation backend, confirm that the usage of
maxContextdoes not exceed the configured interval, and there are no context overflow error reports.
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.