Multi-turn Dialogue and Prompting for Consumer Electronics

What the data for this category looks like

Consumer electronics profit margin data comes from three main sources: offline retail monitoring platforms, e-commerce transaction records, and publicly released supply chain cost statements from brands. Offline channel data updates once daily. E-commerce channel data updates every two hours. Brand supply chain cost data updates once monthly. Each data entry includes these fields: SKU name, model, purchase cost amount, terminal average sales price amount, per-unit sales profit amount, and channel type. All field units are Chinese Yuan. SKU name and model are text fields. Channel type is an enumerated field.

Constraints on multi-turn dialogue and prompting

Differing update frequencies across data sources require multi-turn dialogue to explicitly specify the data time range and channel. This avoids calling outdated or mismatched datasets. The wide range of SKU categories and fixed field dimensions require prompts to tie the association logic between SKU models and fields. This prevents returning market data for unrelated categories. Multi-turn dialogue must retain prior filter conditions. Context breaks will otherwise cause result deviations. All fields use Chinese Yuan as the unit. Prompts must clearly mark the unit to avoid numerical confusion.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`10–15 turns of dialogue context`	Consumer electronics have a large number of SKU categories, and single-turn dialogue involves multiple models and channel details. Limiting context turns avoids interference from redundant information
`prompt_template`	`Call data using the format "channel + SKU model + time range", return the purchase cost amount, terminal average sales price amount, and per-unit sales profit amount fields`	Matches the field structure of consumer electronics data, explicitly specifies the required numerical fields to prevent irrelevant information from being included
`retrieval_top_k`	`Top 8 recall results`	Consumer electronics have a rich variety of SKU categories. Too many recall results cause context overload. 8 results cover market data for mainstream models
`data_refresh_interval`	`Set offline data to 86400 seconds, e-commerce data to 7200 seconds`	Matches the actual update rhythm: offline data updates daily, e-commerce data updates every two hours. This ensures data timeliness
`rerank_threshold`	`0.75`	Filters low-relevance SKU data, preventing non-target category market data from being mixed into dialogue results
`file_parse_chunk_size`	`600–800 characters`	Consumer electronics product documents and financial report fragments are lengthy. This segment length preserves complete per-unit profit and cost information

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 individual analysis, and it is recommended to test on your own samples before finalizing.

Three Common Configuration Mistakes

Phenomenon: Quote links are automatically added to the end of dialogue results. Some users see a "No permission to operate this conversation record" prompt when viewing results. Cause: The reference_visibility parameter is not set to publicly visible, and the prompt template does not include an instruction to disable quote attachments.
Phenomenon: After asking about 3 or more consumer electronics SKUs consecutively in multi-turn dialogue, returned results have missing fields or misplaced numerical values. Cause: The maxContext parameter is set too low, failing to retain SKU models and channel information from prior dialogue, leading to context breaks.
Phenomenon: After uploading consumer electronics retail monitoring documents via the dialogue API, the system cannot parse or recall the documents normally. Cause: The UPLOAD_FILE_ENABLE parameter is not enabled, and the allowed file format whitelist is not configured.

How to Verify Proper Configuration

Initiate a single-turn dialogue, enter a specified SKU model and channel, and confirm the returned results include the preset fields and corresponding units.
Initiate consecutive multi-turn dialogues, ask about the market conditions of different consumer electronics categories in sequence, and confirm the dialogue retains prior filter conditions without information confusion.
View data source update records, and confirm the update frequencies of offline and e-commerce data match the configured data_refresh_interval parameter.
Test the file upload function of the dialogue API, and confirm consumer electronics retail monitoring documents can be parsed and recalled normally.

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.