What the data for this category looks like
This category's data mainly comes from advertising platform backends, attribution systems, CRM lead databases, and landing page statistics tools. Data update cycles are mostly daily or real-time. Daily associated documents include delivery daily and weekly reports, daily price adjustment records, material A/B test tables, and optimization review ledgers. Data fields include impressions, clicks, conversion cost, budget consumption progress, and more. Corresponding units are mostly counts, yuan, cost per thousand impressions. Single data dimensions cover single plan, single material, and single account levels.
What constraints these characteristics impose on multi-turn dialogue and prompt words
The daily or real-time data update cycle of delivery effect optimization requires multi-turn dialogue contexts to synchronize the latest budget consumption and conversion data in a timely manner. This avoids providing adjustment suggestions based on outdated information. Daily associated structured price adjustment records and unstructured review notes require prompt words to adapt to mixed-format inputs. They must distinguish between directly callable structured fields and unstructured content that requires summarization. The multi-level data structure of advertising accounts requires multi-turn dialogue to clearly anchor the context scope of a single plan or single material. This prevents cross-level data confusion. The high-frequency business needs of abnormal indicator positioning and budget allocation require prompt words to preset trigger judgment logic for common abnormal scenarios. This reduces the number of invalid queries.
How to set configurations
The following configurations are based on FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages context length | 8000–12000 token | Adapts to the multi-dimensional fields and document content of delivery data, avoiding truncation of key information |
variables dynamic injection switch | Enabled | Used to quickly inject real-time business data such as real-time budget and conversion cost |
chatId session anchoring rule | Bound to single plan/single material | Matches the multi-level data structure of advertising accounts, avoiding cross-level data confusion |
| Single dialogue history recall limit | Top 10 conversation records | Matches the short-cycle decision-making scenarios of advertising optimizers, reducing redundant context interference |
| Multi-turn dialogue synchronization frequency | Every 300 seconds | Matches daily or real-time data update cycles, ensuring information timeliness |
| Prompt word format verification | Calibrated based on actual testing | Adapts to mixed document formats of price adjustment records and review notes |
Three common mistakes
- No streaming output: The interface does not return content fragments word by word. It only returns results once generation is complete. This occurs when streaming response configuration is not enabled, or the model interface does not support the streaming transmission protocol.
- Extra spaces and uppercase letters appear in output: Unnecessary spaces are mixed into model output content, or letters in knowledge base links are converted to uppercase. This occurs when prompt words do not clearly specify format details, and do not restrict modification rules for link content.
- File parsing tool cannot be invoked: Calling the file parsing tool returns a tool not found or parameter error. This occurs when prompt words do not clearly specify trigger conditions for tool calls, or the tool configuration is not bound to the current session context.
How to confirm configurations are correct
- Verify streaming output: Initiate a conversation that includes a real-time data query. Observe whether the interface returns content fragments word by word. Confirm that the streaming configuration is active.
- Verify dynamic variable injection: Call preset business variables in a conversation. Check whether the returned content includes the latest delivery data. Confirm that the variable configuration is correct.
- Verify context anchoring: Switch to sessions for different plans. Check whether the conversation context only associates data related to the current plan. Confirm that the session binding rule is active.
- Verify history filtering: Initiate a test conversation that includes plugin instructions. Check whether subsequent conversation histories do not include internal plugin execution instructions. Confirm that the filtering rule is active.
The values provided here are common starting points. They should be evaluated against the sample sets used in actual deployment.
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.