Code and Change Review Assistance for Information Flow Advertising Creative Testing: Multi-turn Dialogue and Prompt Engineering

Data for information flow advertising creative testing scenarios comes primarily from advertising delivery platforms, creative material libraries

What the Data for This Use Case Looks Like

Data for information flow advertising creative testing scenarios comes primarily from advertising delivery platforms, creative material libraries, user interaction data, and conversion data. Daily associated documents include creative scripts, material test sheets, delivery data reports, and iteration checklists. Update frequencies are mostly daily or weekly. Data fields include material type, copy content, delivery time slot, click volume, conversion cost, and similar metrics. There are no uniform fixed units. Field formats must match corresponding business documents. Testing operations focus on material combination comparisons. Data volume adjusts dynamically based on test frequency.

Constraints for Multi-turn Dialogue and Prompt Engineering

The scattered nature of data sources requires multi-turn dialogue to support cross-data source context calls, to integrate material, delivery, and conversion data. Daily or weekly update frequencies require dialogue context to match business data timeliness, to avoid using outdated test results. The large number of creative variables and time required to summarize test conclusions demand prompts that can structure and organize comparison results across multiple test datasets. The core scenario of material combination comparisons requires multi-turn dialogue to flexibly switch question logic across different material dimensions. Differences in testing workflows across users require dialogue context to be isolated per session, to avoid data mixing.

Configuration Settings

This configuration applies to FastGPT v4.17.0

Configuration ItemRecommended SettingRationale
chatIdBind the unique user identifier for the current sessionEnable isolation of historical conversations across different users and avoid data sharing
messagesRetain the first 8–12 conversation recordsMatch the timeliness of daily updated data and avoid redundant context overload
variablesBind custom fields from creative scripts and test sheetsDirectly reuse field structures from business documents and reduce manual input costs
Maximum Context Length6000–10000 tokenAdapt to multi-dimensional question requirements for multi-turn creative testing
Historical Conversation Recall LimitFirst 5 entriesBalance context timeliness and model processing efficiency
Prompt TemplateGenerate based on the structure of delivery data reportsMatch field formats from business documents and improve accuracy of test conclusion summarization

Three Common Configuration Mistakes

  • The conversation interface displays "No knowledge base selected", but debugging preview works normally. The symptom is that formal sessions show a "No knowledge base selected" prompt, while debugging mode can call the knowledge base normally. The cause is failure to bind the knowledge base permission configuration for the user session via chatId; only default permissions are used during preview.
  • Different users cannot view their own historical conversations. The symptom is that all users can see all historical records for any initiated conversation. The cause is failure to correctly bind the chatId parameter, leading all sessions to share the same context storage.
  • Model context overflow causes test conclusion summarization to fail. The symptom is truncated model output or a prompt that context length exceeds the limit. The cause is failure to limit the number of retained messages entries, with excessive historical conversations loaded beyond the model's token limit.

How to Verify Successful Configuration

  • Initiate two test sessions using different chatId parameters. Check that the historical records for the two sessions do not overlap, to confirm that user conversation isolation is active.
  • Input multi-turn questions that exceed the preset maximum context length. Observe whether the model responds normally without context truncation error prompts, to confirm that context configuration is reasonable.
  • Bind the variables field from business documents. Initiate a question that includes creative scripts and test data. Check whether the model output matches the business field format, to confirm that variable binding is active.
  • Enter debugging preview mode. Verify that knowledge base permissions load normally, confirm that no "No knowledge base selected" prompt appears, to confirm that permission configuration is correct.

The values given are common starting points and should be measured against the reader's own samples.

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.