What the data for this category looks like
Retail and yield rate data for black appliances comes primarily from industry association retail monitoring databases, public sales reports from major e-commerce platforms, and brand shipment submission data. Data is updated daily with full-channel aggregated data for the previous calendar day. Real-time sales data for some specific product models refreshes every hour. Each data entry includes product SKU code, product model, sales channel category, shipment quantity, terminal average sales price, channel gross profit margin, and regional coverage. Shipment quantity is measured in units, average sales price in yuan, and channel gross profit margin in yuan.
Constraints imposed on multi-turn dialogue and prompt engineering
Data sources are scattered and have multiple dimensions. Multi-turn dialogue must first confirm the user’s required channel, region, and specific model to avoid returning irrelevant data. Data updates occur at a high frequency, so prompts must explicitly specify that only data from the current day’s updated sources be used, to prevent calling expired information. Each data entry has many fields, so multi-turn dialogue must gradually guide users to clarify their required dimensions, to avoid outputting redundant content. Additionally, users often ask follow-up questions about a single product model or region, so full conversation context must be retained to link preceding and subsequent questions.
How to set up the configuration
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
systemPrompt | "Only return daily black appliance market data, including user-specified SKU or model, channel, and region information. Fields are limited to shipment quantity, average sales price, and channel gross profit margin. Units must be clearly marked, and no irrelevant content is included." | Limits output scope, avoids redundant information, and adapts to the fixed format of daily report broadcasts |
maxContext | 8000–12000 characters | Black appliance data has many fields, so sufficient context space is needed to link parameters from multi-turn questions |
retainHistory | Enabled | Supports context association for multi-turn dialogue, meeting user follow-up question needs for specific models or regions |
maxTurns | 5 turns | Controls dialogue length, prevents context overflow, and adapts to the daily report broadcast dialogue scenario |
responseFilter | Filter non-daily data by date | Ensures only daily updated market data is returned, meeting the timeliness requirements of daily report broadcasts |
historyWindowSize | Previous 3 turns of dialogue | Focuses on recent question logic, reducing interference from irrelevant history on current responses |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: After configuring multiple AI dialogue nodes in a workflow, responses from all nodes are displayed simultaneously in the workflow's dialogue interface. Cause: No node output filtering rules for dialogue context are configured, so return content from all nodes is included in the current dialogue context.
- Phenomenon: Users cannot be proactively prompted to supplement key information such as model or region during multi-turn dialogue. Cause: The system prompt only specifies a fixed output format, with no logic reserved for proactively asking for missing parameters.
- Phenomenon: Dialogue return results include expired historical market data. Cause: No time range for data is explicitly specified in the prompt, or no verification rules for data time range are configured.
How to Confirm Proper Configuration
- Initiate a single-turn query, specify a specific model and channel, and verify that returned results only include daily data and that fields meet preset requirements.
- Initiate two progressive queries: first specify only the category, then supplement regional information, and verify that the system proactively asks for missing model or channel information.
- After configuring multiple AI dialogue nodes, test hiding the output of one node, and verify that only permitted response content is displayed in the interface.
- Initiate a query that exceeds the preset maximum number of turns, and verify that the dialogue terminates or prompts that further queries are not allowed.
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.