What Data for This Category Looks Like
Data for in-stream advertising creative testing typically comes from advertising platforms, material libraries, user interaction data, and conversion data. Most updates run on a daily or weekly schedule. Daily output documents include creative scripts, material testing sheets, delivery data reports, and iteration checklists. Core fields cover creative variable combinations, material fatigue scores, click volumes, conversion costs, and compliance status. In the core workflow of this category, creative variable design, material fatigue judgment, and test conclusion summarization are time-consuming. Abnormal issues may cause reduced click-through rates, higher delivery costs, or material compliance violations. Unlike search advertising categories, its core comparison dimension is the combined effect of images, videos, and copy.
Constraints Imposed on Multi-turn Dialogues and Prompt Engineering
Dispersed data sources and fixed update rhythms require multi-turn dialogue processes to gradually guide users to provide exclusive fields for corresponding documents. This avoids missing key information. Fixed structures of daily documents require prompts to preset standardized field extraction rules. These rules adapt to content input in fixed formats such as creative scripts and material testing sheets. Summarization work in time-consuming core links needs multi-turn dialogues to break down test conclusions gradually. This avoids overloading users with one-time, excessive information. The severity of abnormal consequences requires compliance check pre-prompts in the dialogue process. This proactively avoids material compliance risks. Cross-week test iterations need dialogue contexts to retain historical information for longer periods. This matches the weekly data update rhythm.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the advertising account | Ensure each in-stream advertiser’s dialogue history is associated with their exclusive delivery account. FastGPT v4.17.0 supports this configuration |
messages | Retain the first 20 dialogue context entries | Cover variable adjustments and data feedback within a single test cycle, avoiding reasoning delays caused by overly long context |
variables | Inject material testing sheet fields and delivery data cycles | Automatically fill fixed fields of category-specific documents, reducing manual input costs |
| Historical dialogue retention duration | 7 days | Match the weekly delivery data update cycle, ensuring context continuity for cross-week tests |
| Maximum tokens per single reply | 1500 tokens | Adapt to the content length of creative test conclusion summarization, avoiding truncation of key information |
Three Common Misconfigurations
- Issue: Dialogue histories from different advertisers are mixed when
chatIdis not bound. Cause: The dialogue identifier is not associated with the advertiser’s exclusive account, leading to failed context isolation. - Issue: Replies from multiple <AI Dialogue> nodes in a workflow all appear in the final conversation. Cause: Independent context isolation rules are not configured for each workflow node, resulting in untruncated or unisolated output from multi-turn nodes.
- Issue: The question classification node during workflow setup only supports single questions, and cannot support multi-turn question answering and follow-up inquiries. Cause: The prompt does not preset follow-up logic for category-specific documents, and context continuation rules for multi-turn dialogues are not configured.
How to Verify Correct Configuration
- Input a test advertising account identifier, launch the first dialogue, then switch browsers or devices and launch again. Confirm that the dialogue history is not lost.
- After configuring
variables, trigger the prompt in the dialogue. Check that preset material testing sheet fields and delivery data cycles are automatically injected. - Launch a multi-turn test, adjust creative variables, and submit test data. Confirm that the dialogue context retains previous variable information and feedback content.
- Trigger the <AI Dialogue> node in the workflow. Check that only the reply from the current node appears in the final conversation, with no redundant output from historical nodes.
The values provided here are common starting points, and should be evaluated 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.