Multi-turn Conversation and Prompt Engineering for Hotel and

What the data for this category looks like

Data sources for hotel and catering industry research reports include public industry research datasets, publicly disclosed operational data from chain catering enterprises, district commercial bureau business district monitoring reports, and third-party catering supply chain data platforms. Update schedules are divided into multiple tiers: business district foot traffic data is updated weekly, industry trend reports are released quarterly, enterprise operational data is updated alongside annual and quarterly reports, and supply chain price data is updated daily.

The document structure of a single research report includes modules such as regional catering market breakdown, individual store operational metrics, supply chain cost structure, and competitor layout analysis. Operational fields include sales per square meter per business cycle, per-customer transaction value, and table turnover rate per business day. Cost fields include the proportion of food costs in revenue and the proportion of labor costs in revenue, with units respectively: yuan per square meter per business cycle, yuan per customer, times per business day, relative percentage of revenue, and relative percentage of revenue.

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

The multi-tier update schedule of hotel and catering research reports requires multi-turn conversations to dynamically match data from the corresponding cycle, to avoid recalling outdated information from incorrect cycles. The complex document structure requires prompts to clearly specify the recalled document modules, to prevent returning irrelevant supply chain data or competitor analysis content. The differentiated fields and units require prompts to mandate that returned results include corresponding units, to avoid mixing up values for sales per square meter and per-customer transaction value. Multiple data sources require explicitly specifying the data source scope during the conversation flow, to ensure the authority of returned content. Additionally, users may ask about the update time of data during multi-turn conversations, so it is necessary to configure metadata fields that retain data update times to support follow-up question answering.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–12000 characters`	Hotel and catering research reports have long individual content; multi-turn conversations need to retain research report snippets and user questions from multiple rounds of interaction, to avoid context overflow
`recallTopK`	`Top 8–12 results`	Covers multi-dimensional operational and cost fields of hotel and catering research reports, while controlling redundancy of returned content
`promptCustom`	`Must explicitly specify recalled research report modules and attach field units; if cycle-related data is involved, mark the update time`	Adapts to the complex document structure and differentiated field units of hotel and catering research reports, to avoid output confusion
`chunkSize`	`1000–1500 characters`	Balances the integrity of research report paragraphs and recall accuracy; avoids destroying context logic from overly short segments, and affects local recall from overly long segments
`dataSourceFilter`	`Only include two data source types: district business district monitoring and enterprise operational disclosure`	Limits the data source scope to ensure the authority and consistency of returned content
`apiTimeout`	`600 seconds`	Adapts to the batch parsing and recall process of hotel and catering research reports, to avoid timeouts triggered by long content

The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.

Three Common Mistakes

Phenomenon: After calling the conversation interface, the original snippets retrieved from the knowledge base are returned directly without being organized by the large model, resulting in messy formatting and missing field units. Cause: The promptCustom parameter is not configured, and the rules for organizing retrieved content by the large model are not specified; there is a misunderstanding that retrieved results can be returned directly.
Phenomenon: After configuring English prompts, the returned research report content is still in Chinese, resulting in mixed Chinese and English. Cause: The prompt only specifies the output language of the answer, but does not clearly require converting the retrieved Chinese research report content into English, so the conversion of the original retrieved content is not covered.
Phenomenon: When uploading a single hotel and catering research report file to the conversation interface, a 413 Payload Too Large error is triggered. Cause: The UPLOAD_FILE_MAX_SIZE parameter is not adjusted to a value suitable for the size of the research report file, resulting in failure of file upload verification.

How to Confirm Proper Configuration

Initiate a single-round test conversation, ask a question that includes sales per square meter and per-customer transaction value, check whether the returned results include corresponding units, and adjust promptCustom until the format meets requirements.
Initiate a multi-turn conversation: first ask about catering data for a specific business district, then follow up with a question about the update cycle of that data, check whether the system can correctly retain context and provide an answer, and adjust maxContext until no context is lost.
Upload a single hotel and catering research report file, call the conversation interface, check whether the returned results only include the specified research report modules, and adjust dataSourceFilter and recallTopK until the expected results are achieved.
Check the interface call credential configuration, verify the matching relationship between appId and the secret key, ensure the authentication process works normally, and adjust the secret key configuration until the interface returns a normal status code.

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.