Multi-turn Dialogue and Prompt Engineering for Consumer

Data profile for this category

Consumer electronics investment research data sources include terminal shipment ledgers, supply chain quotation sheets, product specification documents, industry research reports, and patent databases. Update frequencies are divided into real-time supply chain quotations, weekly terminal sales data, and monthly quarterly revenue reports. Document structures cover structured fields such as material codes, unit prices, and delivery times, single product specification documents spanning thousands of characters, and quarterly industry analysis documents spanning tens of thousands of characters. Fields include shipment volume (unit: ten thousand units), material cost (unit: yuan), patent application numbers, and others.

Constraints on multi-turn dialogue and prompt engineering

The consumer electronics investment research data has a large number of structured fields, wide variation in document length, and significant differences in update frequencies. These factors create multiple constraints for multi-turn dialogue and prompt configuration. It is necessary to clearly define the instruction boundary between structured field extraction and unstructured analysis in prompts, to avoid confusion between unit rules for material codes and shipment volume. Segmented recall for long documents must match the context window size of the dialogue, to prevent truncation of critical supply chain information. Frequently updated quotation data requires multi-turn dialogue to automatically trigger recall verification of the latest data, to avoid using expired material cost data. The time range for patent data must be clearly specified in the prompt, to ensure recalled patent information meets investment research cycle requirements.

How to configure the settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	A single consumer electronics research report can reach tens of thousands of characters. Multi-turn dialogue needs to retain 3-5 rounds of context to avoid truncating critical supply chain data
`recallTopK`	`Top 6–8 entries`	Consumer electronics data includes structured ledgers and unstructured research reports. Too many recalled entries will disrupt dialogue logic, while too few will fail to cover all supply chain information
`promptTemplate`	Fixed to include "Please prioritize using supply chain data updated in the last 7 days, and unify shipment volume units to ten thousand units"	Consumer electronics data has large differences in update frequencies. Clear unit and time range constraints must be specified to avoid deviations in investment research conclusions
`chunkSize`	`1000–1500 characters`	The length of individual consumer electronics product specification documents is concentrated between 1000 and 3000 characters. Segmentation allows precise matching of context recall for multi-turn dialogue
`apiResponseFilter`	Only return the "Final Answer" field	Intermediate dialogue results inserted in some workflows do not need to be displayed in the final output, to avoid redundant information interfering with users
`similarityThreshold`	`0.75–0.85`	Consumer electronics supply chain fields have high matching accuracy requirements. A threshold that is too low will recall irrelevant material data, while a threshold that is too high will fail to match similar product models

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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

Phenomenon: The final workflow output includes intermediate AI dialogue content. Cause: The apiResponseFilter parameter is not configured, and intermediate dialogue results are not filtered.
Phenomenon: Irrelevant document titles appear in API call return dialogue results. Cause: A reasonable threshold for the similarityThreshold parameter is not set, and documents that do not match the query are recalled.
Phenomenon: The same supply chain field is repeatedly asked during multi-turn dialogue. Cause: Context reuse rules are not clearly specified in the prompt, leading to repeated triggering of the same knowledge base recall.

How to verify a correct configuration

Initiate a dialogue that includes a structured field query, check whether the units of the returned results match the preset rules, and adjust the promptTemplate until the units are unified.
Insert an intermediate dialogue node and initiate a complete workflow test, check whether the final output only retains the target answer content, and adjust the apiResponseFilter until intermediate results are filtered out.
Initiate a query related to long documents, check whether the recalled document segments cover key information, and adjust chunkSize and recallTopK until they match business requirements.
Call the API interface to view the returned fields, check that only the expected answer content is included, and confirm that the apiResponseFilter configuration is effective.

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.