What Core Data for This Category Looks Like
Core data for campaign setup originates from advertising platforms, user profile databases, budget scheduling tables, and attribution tools. Update frequency follows a daily cadence or within a single campaign cycle. Daily deliverable documents fall into four categories: campaign setup outlines, audience targeting settings, budget allocation details, and conversion tracking configurations. Targeting settings include fields such as region, age, and interest tags. Budget scheduling includes start and end dates, plus daily spending limits. Conversion tracking must align with event IDs and reporting paths from attribution tools. All fields must map to compliant configuration items for the associated advertising platform. FastGPT v4.17.0 supports multi-turn dialogue data calls for this use case.
Constraints for Multi-turn Dialogue and Prompt Engineering
Multi-source data sources require multi-turn dialogue to support dynamic calls of cross-platform fields, avoiding configuration deviations caused by hardcoding. Daily or campaign-level update frequency requires prompts to limit the time range of context, ensuring that called budget and targeting data use the latest available versions. Four fixed document structures require multi-turn dialogue to guide users to supplement fields in a fixed order, reducing omissions. Strong binding between fields and units requires prompts to clearly specify the format and units of each field. For example, budget must be labeled "yuan", and targeting tags must match official enumeration values from the advertising platform. Additionally, the most time-consuming audience targeting and budget allocation steps for this category require multi-turn dialogue to break down requirements incrementally, preventing model confusion caused by excessive input at one time.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window length | 8000–10000 characters | Campaign setup documents typically include multiple sets of targeting, budget, and conversion configurations. Limiting the window avoids model confusion from overly long content |
| Number of multi-turn dialogue history entries to retain | First 6 interactions | Demand iterations for campaign setup mostly focus on targeting, budget, and conversion links. Retaining key history reduces repeated confirmations |
| Prompt variable configuration | Bind variables parameter to import targeting tag library | Targeting options from advertising platforms must be called, and dynamic loading via variables avoids hardcoding |
| Dialogue field validation switch | Enabled | Campaign setup requires mandatory fields, and validation can prevent startup failures in advance |
| Dialogue timeout setting | 600 seconds | Multi-turn adjustments for complex campaign plans require sufficient time, and timeout prevents unnecessary resource occupation |
| Uploaded file size limit | 100 MB | Matches the conventional maximum volume for campaign setup documents, avoiding parsing failures for large files |
Three Common Configuration Mistakes
- Uploaded campaign setup documents cannot be parsed, and no error prompt appears in the background: No parsing template for advertising campaign documents is configured, or the uploaded file size exceeds system limits
- Formatting is lost after copying AI-generated campaign setup content to other tools: The native format retention switch for dialogue output is not enabled, or the prompt does not specify output format as standard markdown
- Generated campaign setup content is not polished and expanded as required, only outputting a basic framework: No polishing and expansion instructions are included in the prompt, or the
messagesparameter is not bound to pass the original output content
How to Verify Successful Configuration
- Upload a test campaign setup document, and check whether targeting, budget, and conversion fields can be correctly extracted
- Initiate a multi-turn dialogue to adjust targeting settings, and check whether context history is correctly retained
- Call the dialogue termination interface, pass the test
chatId, and check whether the dialogue is normally interrupted - Copy the AI-generated document content, paste it into a rich text editor, and check whether the markdown format is complete
Note: The configuration values listed above are common starting points. Adjust them based on your own specific samples and use cases.
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.