Multi-turn Dialogue and Prompt Engineering for Black Goods

What this category's data looks like

Black goods investment research data comes from multiple sources. These include industry association public reports, official brand technical specification documents, e-commerce platform sales parameter pages, third-party testing agency performance and energy efficiency reports, and upstream supply chain raw material quotation data.

Data update frequency fluctuates with new product launches and industry standard adjustments. Core parameters are updated synchronously when new products launch. Industry reports are updated quarterly. Raw material prices are updated daily.

Document structures include standardized technical parameter tables, competitor comparison documents, marketing content, and after-sales feedback data. Common fields include SKU number, launch date, cooling capacity, energy efficiency rating, overall dimensions, and suggested retail price. Corresponding units are Watt, rating, millimeter, and RMB yuan.

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

Black goods investment research data includes multi-dimensional standardized parameters and non-standardized market content.

Multi-turn dialogue must distinguish between three user request types: technical parameter queries, competitor comparisons, and market updates. Prompts must explicitly bind SKU identifiers from historical conversations. This avoids confusion between different models.

Update frequencies are inconsistent. Multi-turn dialogue must prompt calls to the latest data sources. This prevents referencing outdated product parameters.

There is a risk of mixed unit usage across fields. Prompts must automatically calibrate unit conversion logic.

Multi-turn dialogue often involves multi-round parameter comparisons. Sufficient historical context must be retained to support continuous investment research queries.

How to set configurations

Configuration Item	Recommended Range	Rationale
`maxContext`	`8000–12000 characters`	Single black goods technical documents have relatively long lengths. Multi-turn dialogue must retain sufficient historical key information such as SKU numbers and energy efficiency ratings. This avoids critical data loss caused by context overflow.
`recallTopK`	`Top 10–15 results`	Black goods investment research data includes multi-dimensional parameters. Too many recalled results will disrupt dialogue logic. Too few results will fail to cover competitor comparisons and technical details required by users.
`similarityThreshold`	`0.75–0.85`	Filter general home appliance documents unrelated to black goods parameters. Accurately match targeted investment research fields such as cooling capacity and dimensions. This reduces irrelevant recalled content.
`rerankTopN`	`Top 5–8 results`	Retain the most relevant technical reports and competitor data after re-ranking. Adapt to in-depth investment research queries in multi-turn dialogue. This avoids interference from redundant information.
`systemPrompt`	Calibrated based on actual testing	Explicitly distinguish three investment research scenarios: technical parameter queries, competitor comparisons, and market updates. Bind SKU identifiers from historical conversations. Unify unit conversion logic.
`historyWindowSize`	`10–15 turns`	Black goods investment research dialogue often involves multi-round parameter comparisons. Retaining sufficient history avoids repeated asking of model and parameter information already provided by the user.

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 targeted analysis. It is recommended to test on your own samples before finalizing settings.

Three common mistakes

Setting maxContext too small. When the dialogue exceeds the threshold, historical SKU numbers are lost. The interface prompts that the currently discussed home appliance model cannot be identified. This occurs because the requirement to retain SKU identifiers for black goods multi-turn dialogue is not addressed. The long context requirements of this category are not matched.
Receiving a 400 status code (no body) error when calling database tools. This occurs because the query statement containing black goods SKU numbers is not formatted correctly. Or the database connection timeout setting is too short, causing the request to be interrupted before completion.
Not setting historyWindowSize, leading to dialogue history overflow. Subsequent queries cannot associate previously mentioned energy efficiency rating data. This occurs because the long context characteristics of black goods investment research dialogue are not considered. Sufficient historical dialogue turns are not retained.

How to confirm correct configuration

Initiate multi-turn queries with specific SKU numbers. Verify that subsequent conversations can automatically associate previously mentioned model parameters. No repeated asking for model information is needed.
Call database tools to execute queries containing black goods SKU fields. Confirm that no 400 status code (no body) error is returned.
Check the context length of the conversation history. Confirm that key technical parameters and SKU information can still be retained after exceeding the maxContext threshold.
Trigger a manual knowledge base refresh. Confirm that updated new product data can be correctly recalled by multi-turn dialogue. This data can then be used for subsequent investment research comparison queries.

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.