Multi-turn Dialogue and Prompt Engineering for Apparel and

What the data for this category looks like

Apparel and home textiles industry due diligence data primarily comes from brand SKU ledgers, fabric batch quality inspection reports, supplier supply lists, and e-commerce platform sales snapshots. Data update cadence aligns with new product seasons. Core SKUs are updated quarterly, and batch quality inspection reports are updated in real time with production batches. Most documents are Word format around 10 MB. They contain fields including SKU codes, fabric composition, gram weight, washing standards, supplier information, and quality inspection report numbers. Common units include g/㎡ for gram weight, yuan/meter for fabric price, and pieces/sets for finished product price.

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

Large, multi-field documents increase parsing time. Adjust parsing timeout parameters to address this. Parallel queries for multiple SKUs need unique identifiers to avoid parameter confusion during multi-turn dialogue. Diverse and easily mixed-up field units require prompts to explicitly specify unit rules. Frequently updated batch data requires regular knowledge base refreshes to avoid returning outdated information.

How to set configurations

Configuration Item	Recommended Setting	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`900 seconds`	Apparel and home textiles due diligence reports often include multiple batches of quality inspection data and multi-SKU ledgers, with individual files exceeding 10 MB. Extended parsing timeout is required
`UPLOAD_FILE_MAX_SIZE`	`15 MB`	Accommodates the maximum size of multi-SKU summary documents common in the industry
`RECALL_COUNT`	`Top 8 entries`	A single due diligence report contains multiple sets of fabric, price, and supplier data. A sufficient number of recalled entries is needed to cover user query dimensions
`SIMILARITY_THRESHOLD`	`0.72–0.78`	Distinguishes fabric composition and gram weight parameters of similar SKUs, avoiding confusion between identical products from different batches
`maxContext`	`6000–8000 characters`	Tracks query context for different SKUs during multi-turn dialogue, preventing loss of critical identifier parameters
`PROMPT_PREFIX`	Calibrated based on actual testing	Must explicitly specify units for returned fields (such as g/㎡, yuan/meter) to align with industry data format specifications

The parameter values provided on this page are common 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 settings.

Three common mistakes

Issue: Parsing times out for individual apparel and home textiles due diligence documents over 10 MB, returning 504 Gateway Timeout. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter was not adjusted, and the default timeout duration is insufficient for large files containing multiple batches of quality inspection data.
Issue: Fabric gram weight and supplier information for different SKUs are confused during multi-turn dialogue, with empty fields returned in results. Cause: maxContext was not configured to limit context length, or the prompt did not explicitly require binding SKU codes as query identifiers.
Issue: Debug preview is unresponsive after enabling input guidance, returning 400 Bad Request. Cause: The custom thesaurus does not use apparel and home textiles-specific fields (such as fabric composition, washing label requirements) as guidance keywords, and the format does not meet platform validation rules.

How to confirm correct configuration

Upload a single 12 MB apparel and home textiles SKU summary document, and verify that parsing completes within 10 minutes with no timeout errors.
Initiate two rounds of dialogue, querying gram weight parameters for different SKUs respectively, and confirm that returned results are bound to the corresponding SKU codes with no parameter confusion.
Adjust SIMILARITY_THRESHOLD to 0.75, test querying SKUs with similar fabric compositions, and confirm that recalled results have no cross-batch confusion.
Enable input guidance, enter "fabric composition", and verify that the debug preview pops up corresponding industry-specific guidance options.

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.