Multi-turn Dialogue and Prompt Engineering for E-commerce

What the data for this category looks like

E-commerce service marketing content data in financial scenarios primarily originates from the product management module of affiliated e-commerce platforms, marketing activity configuration centers, user consultation session logs, and order association tag libraries. Data update schedules align with marketing activity launches: real-time updates when new products go live or activities start. Daily maintenance cycles do not exceed 24 hours. The structure of a single data entry includes product unique identifier, marketing copy body, delivery channel type, user interaction tags, and conversion association fields. Field units include character count, click count, conversion order count, and others. Some fields support storage in multiple language versions.

What constraints these characteristics impose on multi-turn dialogue and prompt engineering

The characteristics of e-commerce service marketing content data in financial scenarios create multiple constraints for multi-turn dialogue and prompt configuration. Real-time updated content requires the dialogue system to pull the latest data sources first. Do not rely on cached content older than 24 hours. Fields stored in multiple languages require explicit specification of the target language for marketing copy in prompts to avoid cross-language output errors. Interaction tags from user consultation logs must be retained as context. This ensures multi-turn dialogue does not require repeated disclosure of product identifiers or activity topics. The large size of individual product detail data requires multi-turn context length to accommodate long text input. This prevents truncation of critical information.

How to set the configurations

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	The combined single-round context of e-commerce product details and marketing activity copy typically falls within 6000 characters, with buffer space reserved to prevent truncation of critical information
`knowledgeBaseRefreshInterval`	`86400 seconds`	The daily maintenance update cycle for e-commerce marketing content does not exceed 24 hours. Regularly refreshing the knowledge base ensures content stays synchronized with the platform
`multiRoundMemoryEnable`	`Enabled`	E-commerce user consultations often involve multi-turn questions about products or activities. Retaining historical interaction tags and product identifiers prevents repeated inquiries
`promptTemplate`	`Prioritize matching product IDs/activity names mentioned by users, associate historical interaction context`	Adapt to e-commerce consultation scenarios, accurately recall corresponding marketing content, and avoid generic responses
`citeEnable`	`Enabled`	Marketing content must be linked to source data to ensure replies can trace activity rules and product details
`maxCiteCount`	`Top 3`	Single-round e-commerce consultations typically focus on 1-2 products. Limiting the number of citations avoids dispersing the core logic of the dialogue

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.

Three common configuration errors

Issue: Dialogue interface calls return results that do not include the cite_id field. Cause: The citeEnable configuration item is not enabled, or source citation collection is not enabled in the knowledge base configuration.
Issue: Multi-turn dialogue cannot associate previously mentioned product identifiers, and subsequent replies do not match corresponding marketing content. Cause: The multiRoundMemoryEnable configuration is not enabled, or the historical context length exceeds the limit, leading to truncation of critical information.
Issue: After configuring debugShowThought as enabled, the AIProxy runtime outputs thinking processes, but this content is not displayed in the conversation history. Cause: Conversation history only stores user and assistant interaction text, and debug output is not written to session logs by default.

How to verify correct configuration

Initiate a test conversation that includes specific product identifiers and historical consultation content. Verify whether the reply matches the corresponding marketing copy.
Call the dialogue interface. Check whether the returned results include the cite_id field and associated source information.
View the conversation history record. Confirm that previously mentioned product identifiers, activity topics, and other context are correctly retained.
After configuring debugShowThought as enabled, view the AIProxy runtime logs and conversation interface. Confirm the correspondence between debug output and conversation content.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.