What the data for this category looks like
Rural commercial bank marketing content data primarily comes from internal legacy promotional materials, including branch leaflets, official account posts, local customer return visit script templates, short video scripts, and more. Update cycles follow local marketing themes and product adjustments, with no fixed schedule. Bulk updates occur at quarterly nodes or when new products launch.
Documents are organized by product type, applicable customer group, and promotional channel. Fields include material unique identifier, applicable scenario, script text, and customer feedback tags. Most field types are text and enumeration values, with no unified fixed units.
What constraints these characteristics impose on multi-turn dialogues and prompting
The segmented local customer group material structure requires multi-turn dialogues to retain context about customer group attributes and channel information, to prevent cross-scenario confusion.
Non-fixed update cycles for materials require prompts to support dynamic calls to the latest materials, and cannot rely on hard-coded static content.
The categorized document structure requires retrieval rules to match classification dimensions, to reduce interference from irrelevant materials.
The customer feedback tag field requires the dialogue process to adjust script direction based on historical feedback, to improve the adaptability of marketing content.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Matches the locally segmented scenario of rural commercial bank marketing materials, retains context information such as customer group and channel in multi-turn dialogues, and avoids model output deviation caused by context overflow |
recall_top_k | Top 6–8 entries | Adapts to the number of categories of rural commercial bank marketing materials. Too many retrievals will distract users, while too few cannot cover the actual needs of segmented customer groups |
prompt_template | Retrieve local marketing materials categorized by customer group, product, and channel, output colloquial scripts adapted to branches or official accounts, disable Markdown formatting | Matches the categorized structure of rural commercial bank marketing materials, clarifies output requirements, and aligns with local customer group communication habits |
auto_save_conversation | Enabled | Retains historical context of multi-turn dialogues, avoiding the need to restart a session for each communication |
response_format | Plain text | Adapts to display scenarios for mobile terminals and offline branches, preventing formatting abnormalities caused by Markdown |
enable_history_summary | Enabled | Compresses overly long dialogue contexts, reducing redundant costs of model calls |
The parameter values provided on this page are common recommended starting points for establishing configuration baselines. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Symptom: Dialogue results must be viewed manually in the sidebar and cannot be output directly in the main dialog box. Cause: The
response_inlineparameter is not enabled, or theresponse_panel_switchis configured to the off state. - Symptom: A new session must be created for each consultation, and historical communication context cannot be continued. Cause: The
auto_save_conversationparameter is not enabled, or the session expiration time is set to less than300 seconds, causing the context to be cleared. - Symptom: AI output content always contains Markdown syntax elements. Cause: Markdown formatting is not explicitly disabled in the
prompt_template, or theresponse_formatis configured as a Markdown type.
How to verify correct configuration
- Launch a multi-turn consultation regarding local farmer loans, verify that response content is output directly in the main dialog box, and confirm that the
response_inlineparameter configuration meets business requirements. - Launch 3 consecutive consultations on marketing content for the same customer group, verify that the session retains historical context, and confirm that the
auto_save_conversationandmaxContextparameter configurations match the business scenario. - Check AI output content, confirm that no Markdown formatting elements appear, and verify that the
prompt_templateandresponse_formatparameter configurations meet requirements. - Launch a multi-user test session, verify that dialogue contexts for multiple users are independently isolated, and confirm that the
conversation_isolationparameter configuration is correct.
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.