What the data for this category looks like
The data for consumer electronics marketing content primarily comes from partner brand materials provided by financial institutions, in-house marketing campaign assets, and user support tickets. Update cadence aligns with new product launches and marketing milestones, with no fixed cycle. Bulk new product information is added when new models launch, and script templates and activity rules are updated before promotional campaigns. The document structure is divided into three parts: product parameter libraries (including SKU numbers, functional parameters, applicable financial scenario tags), marketing script templates (categorized by customer acquisition scenarios such as installment payments, point redemption, and sweepstakes), and user frequently asked question libraries (sorted by device type). Fields include device model, parameter unit, activity effective time, and applicable customer group tags. Field configurations vary across different brand materials.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Dispersed data sources and inconsistent formats require multi-turn dialogue to first identify the device model and financial scenario mentioned by the user, to avoid retrieving mismatched marketing content. Irregular update frequency requires prompts to support rapid adaptation to new product fields and activity rules without hardcoding. The layered document structure requires multi-turn dialogue to first anchor the target product before calling the corresponding knowledge base. Fields include units and scenario tags, so prompts must require correct units and scenario descriptions to be included in outputs to avoid confusion. User questions are typically progressive—for example, first asking about installment interest rates, then device parameters. Multi-turn context must retain the user-specified device and activity information without repeated inquiries.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Multi-turn dialogue for consumer electronics marketing content typically includes device models and financial scenario parameters. This range covers complete context to avoid losing critical SKU and activity information |
similarity threshold | 0.78–0.82 | Parameters across different consumer electronics models have high similarity. This range filters irrelevant retrievals while retaining accurately matched marketing content |
retrieval count | Top 5–7 entries | Marketing materials include multi-dimensional information. 5–7 entries can cover product selling points, parameters, and customer acquisition scripts without overloading context |
globalVariablePersist | Enabled | Multi-turn dialogue must retain user-specified global variables such as device SKU and activity time, avoiding repeated inquiries |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Consumer electronics marketing documents typically include long paragraphs of parameter descriptions. 120 seconds enables complete parsing of long documents |
promptTemplate | First identify the device model and financial scenario, then call the corresponding knowledge base. Outputs must include parameter units and activity tags | Consumer electronics marketing content must match user financial needs. Anchoring scenarios and models avoids retrieving mismatched content |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, 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 mistakes
- Symptom: Returns the error
Cannot read properties of null (reading 'q')when calling the workflow. Cause: Multi-turn dialogue context fails to correctly pass the user's original question field, resulting in an emptyqparameter when the workflow triggers. - Symptom: The question classification model cannot accurately identify device models and financial scenarios, leading to mismatched retrieved marketing content. Cause: Uses a general model not fine-tuned for consumer electronics and financial scenarios, which cannot recognize dedicated fields such as SKU numbers and parameter units.
- Symptom: After the user changes the device model they are asking about in a multi-turn dialogue, the system still uses previous product information. Cause: Global variable persistence is not enabled, and the device SKU variable in the session is not updated in a timely manner.
How to confirm correct configuration
- Initiate a multi-turn test dialogue that includes financial scenarios and device models, verify that the system retains previously mentioned activity and device information without repeated inquiries.
- Upload consumer electronics marketing documents and financial activity rules from different brands, initiate questions corresponding to target scenarios, and verify that retrieved knowledge base content matches the target product and activity.
- Trigger a workflow call, check logs for valid values of the
qparameter, and confirm there are no empty field errors. - Adjust the similarity threshold, initiate questions with similar parameters, and verify that the accuracy of retrieval results meets expectations.
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.