What the Data for This Category Looks Like
Jewelry data comes from internal brand product management systems, e-commerce listing ledgers, and supply chain delivery manifests. New product launches follow quarterly release cycles and holiday marketing events. Old products are removed when inventory is depleted. Small quantities of restocked SKUs are updated on an ongoing basis. Data is structured as a single-row table or JSON entry per SKU, with fields including item number, material, chain length (centimeters), gram weight (grams), recommended selling price (yuan), applicable scenarios, and design theme. Each field has clear unit and attribute definitions.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Jewelry has many fields with clear units. Multi-turn dialogue must avoid mixing up size, price and other parameters of different SKUs. Prompts must require explicit reference to the target SKU’s item number or attributes. Frequent SKU updates require the knowledge base to retrieve real-time data. Multi-turn dialogue must limit the length of retained historical context to avoid including expired inventory or price information. Applicable scenarios and design themes are core to marketing content. Multi-turn dialogue must guide users to clarify their scenario requirements step by step. Prompts must tie product attributes to marketing scenarios, and avoid generating generic, untargeted copy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000–3000 characters | A single jewelry SKU data entry is approximately 100–200 characters. Retaining 2–3 SKU contexts avoids model context overflow, while covering the core needs of the current conversation |
Recall count | Top 6–8 entries | Jewelry products in this category have high similarity in attributes. Too many retrieved entries will make the prompt too long. Too few will miss precisely matched SKUs |
Similarity threshold | 0.72–0.78 | There are many jewelry items with the same material and design. A threshold that is too low will introduce irrelevant SKUs. A threshold that is too high will fail to match users’ specific needs |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Bulk uploaded jewelry SKU ledgers usually contain hundreds of entries. Parsing requires a longer processing time |
UPLOAD_FILE_MAX_SIZE | 50 MB | When combining product images and data ledgers for jewelry uploads, single file capacity usually does not exceed this threshold |
promptTemplate | "Based on the scenario requirements provided by the user, match corresponding jewelry SKUs from the knowledge base, output marketing copy including item number, dimensions, and applicable scenarios, introduce no more than 3 SKUs per conversation" | Jewelry marketing content must accurately match scenarios, and avoid information overload that makes it difficult for users to choose |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: In multi-turn dialogue, the AI’s latest marketing copy includes SKU information from previous replies. Cause: No truncation rule is set for
maxContext, so historical context accumulates continuously and exceeds the model’s processing limit. - Issue: Input guides and word banks are configured, but preset guide questions do not display in the conversation interface. Cause: The switch to show input guides in the conversation interface is not enabled, or keywords in the word bank are not bound to guide questions.
- Issue: After uploading an XLSX-format jewelry product ledger, the conversation cannot read the file content. Cause: Structured parsing configuration for XLSX files is not enabled, or the file contains non-standard formats such as merged cells or hidden columns, leading to parsing failure.
How to Verify Successful Configuration
- Run a test query that includes multiple jewelry attributes. Confirm the AI’s reply only contains SKU information from the current conversation context, with no redundant historical content.
- Access the conversation interface preview. Confirm preset guide questions display normally, and corresponding word bank content matches when input trigger words are used.
- Upload a standard test XLSX jewelry ledger. Run a query to repeat the file content. Confirm the AI can accurately extract fields and units.
- Adjust the value of
maxContext. Test historical conversations of different lengths. Confirm replies do not experience context overflow or information loss.
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.