What the Data for This Category Looks Like
Typical users of this category include growth product managers, front-end operations specialists, and ad optimization specialists. Data sources include landing page access logs, heatmaps, form submission data, and ad source parameters. Daily associated documents include landing page plans, tracking requirement documents, AB test reports, and form field lists. Data updates occur weekly or are adjusted per individual marketing campaign. The most time-consuming daily tasks are page path analysis, form blockage troubleshooting, and experimental result evaluation. Data anomalies or processing errors may lead to lead loss, inability to attribute data, and increased ad delivery costs.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Scattered data sources and linked multiple document types for landing page conversion optimization require multi-turn dialogue to bind the chatId of a single access to retain contextual association. Data updates are adjusted per campaign, so prompts must dynamically adapt to the current form field list and tracking requirements. Daily time-consuming tasks focus on path analysis and result judgment, so multi-turn dialogue must preset processes to quickly retrieve historical analysis results. Additionally, documents such as landing page plans and AB test reports must be passed as knowledge base materials, and exclusive parameters for the current campaign must be passed via variables to avoid repeated calls to irrelevant data. The messages structure must also be used to standardize the input and output format of each dialogue round to align with landing page business logic.
Configuration Guidelines
The following configuration instructions apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the unique identifier of the landing page access session | Ensure multi-turn dialogue retains the complete contextual association of a single user from ad click to form submission |
messages | Include the current form field list and attribution rules in the initial system prompt, append user questions and AI responses for each round | Adapt to the exclusive business scenario of landing page conversion optimization, avoid context loss |
variables | Pass the current campaign's ad source parameters and form field list | Dynamically load current business data without modifying prompts each time |
| Maximum context token count | 800–1200 tokens | Match the average length of landing page-related documents, avoid exceeding processing limits |
| Single search limit | Top 3 knowledge base results | Focus on content most relevant to the current landing page plan, reduce invalid information interference |
| Dialogue timeout duration | 600 seconds | Match the average waiting time of landing page users, avoid premature session termination |
Common Misconfigurations
- Conversation log labeling fails to function, only raw conversation content is displayed. The cause is failure to enable the automatic labeling switch for session logs, and failure to perform classification tagging by associating the landing page access
chatId. - AI output is displayed directly on the page without processing via a text splicing component. The cause is failure to configure the output delay logic of the dialogue node, directly mounting the AI response to the page DOM, and not performing unified output via the specified component per business requirements.
- Knowledge base retrieval results cannot be correctly passed to the AI dialogue node. The cause is failure to pass knowledge base reference data in the required
variablesformat, preventing the AI from recognizing the retrieved landing page document content.
How to Verify Proper Configuration
- Initiate a simulated landing page user question, check if the dialogue context retains the previous round's form field information, and confirm that the
chatIdbinding is effective. - Trigger the AI dialogue node, verify that the output first enters the text splicing component, and displays uniformly after all data processing is complete, confirming that the output logic meets configuration requirements.
- Pass simulated knowledge base retrieval results, check if the AI can correctly reference content from the landing page plan, and confirm that the
variablesdelivery format is correct. - View the session logs, confirm that each dialogue carries the exclusive identifier of the landing page access, and that the labeling function operates correctly.
The values provided 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.