What the data for this category looks like
Snack food market data is primarily sourced from offline retail POS systems, online e-commerce platform sales backends, and industry supply chain monitoring platforms. Data updates occur once daily, generating a daily market report document. The document uses a structured data table, with individual SKU as the base dimension. Fields include SKU ID, product name, packaging specification, daily average transaction price, daily sales volume, channel sales share, and average transaction price for the same period last week, among others. The unit for average transaction price is yuan per unit packaging. The unit for sales volume is pieces. Sales share is presented as a proportional value, not marked as a percentage.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Dispersed data sources mean users may need to specify online or offline channels when making queries. Multi-turn dialogue must first confirm the required data source dimension to avoid returning cross-channel mixed data. The daily update feature requires prompts to clearly define the query date range, preventing the AI from calling expired data. The multi-SKU document structure requires dialogue to retain SKU ID and packaging specification information, avoiding confusion between products of the same category with different specifications. The design of multiple fields and varying units requires prompts to guide users to clearly specify the query field, and unify unit formats in responses to avoid unit confusion. Additionally, multi-turn dialogue must retain the previous round's SKU and date information to avoid repeating the same questions and improve interaction efficiency.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Snack food market daily reports contain multi-field data across multiple SKUs. Multi-turn dialogue needs to retain context such as SKU, date, and query fields to avoid information loss from context overflow |
json_schema | Define structured return format as needed, example: {"type":"object","properties":{"sku":{"type":"string"},"date":{"type":"string"},"price":{"type":"number"},"sales":{"type":"integer"}},"required":["sku","date"]} | Standardize response format to meet user demand for standardized JSON output and facilitate subsequent data processing |
recall_top_k | Top 3–5 entries | A single market daily report contains data for dozens of SKUs. Too many recalled entries increase context load, while too few cannot cover the target SKU of the user's query |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Snack food market daily report documents usually contain batch data across multiple days and SKUs, which requires a longer time to complete parsing |
rag_relevance_threshold | 0.75–0.85 | Distinguish valid SKU data from irrelevant data to avoid recalling market information from non-target categories |
system_prompt | Fixed constraints: Only respond based on uploaded snack food market daily report data, clearly indicate the queried SKU and date, return according to specified fields, unify units to yuan per unit packaging and pieces | Restrict the AI's response scope to avoid generating irrelevant content, and unify response format and units |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: A
504 Gateway Timeoutstatus code appears during multi-turn dialogue, and the response time exceeds expectations. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the default timeout period is too short to complete full parsing of multi-SKU market report documents. - Phenomenon: The JSON format of the AI's response does not meet preset requirements, with missing fields or chaotic unit markings. Cause: The required fields and unit specifications were not clearly constrained in
system_prompt, or thejson_schemaconfiguration does not cover all fields that need verification. - Phenomenon: The AI repeatedly asks for the same SKU or date information during multi-turn dialogue. Cause: The
maxContextparameter was not configured correctly, and the context window is too small to retain key query information from the previous round of dialogue.
How to Verify Proper Configuration
- Upload a test snack food market daily report document, initiate a single-round query, and check whether the returned fields and units meet preset requirements.
- Initiate two consecutive rounds of dialogue: first query the average transaction price of a specific SKU, then query the daily sales volume of that SKU. Check whether the AI automatically associates the previous round's SKU information without repeating the question.
- Adjust the
rag_relevance_thresholdparameter, initiate a query covering multiple categories, and check whether the returned results only include snack food-related data. - Export the content of the AI's response, and check whether it conforms to the preset JSON format, with no extra fields or non-compliant format issues.
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.