What the data for this category looks like
Data related to commercial property yield rates comes from rent collection records in internal property operation management systems, public area energy consumption settlement documents, and third-party commercial real estate market databases. Update frequencies align with business nodes: rent data is synced monthly, energy consumption data updates with each billing cycle, and surrounding reference data updates weekly. Most supporting documents are structured tables, with fields including project ID, number of leased units, total actual collected rent, actual usable area, monthly energy expenditure, and surrounding similar-property rent reference values. Units include square meters, yuan, calendar days, and others. Field details vary across individual projects.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Multi-source data has inconsistent update cycles. Multi-turn dialogue must clearly distinguish the time ranges of different data, and prompts must specify the update cycle each data belongs to. There are many structured fields with close relationships. Multi-turn dialogue must track the specific dimensions the user focuses on, to avoid confusing data from different projects or types. Field details vary across individual projects. Prompts must guide users to clearly specify the fields they are interested in, to reduce discrepancies in data definitions. Third-party reference data and internal operation data come from different sources. Dialogue must clearly label data sources to avoid misleading judgments.
How to Configure
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 2000-4000 characters | Commercial property data has many fields. Multi-turn dialogue must retain multiple sets of context such as rent, energy consumption, and project ID to avoid truncating critical information |
systemPrompt | Must clearly label data sources and update cycles, distinguish between internal operation data and third-party reference data, and only return values for fields specified by the user | Adapt to caliber differences and update cycle requirements of multi-source data |
contextRecallCount | Top 6 entries | Commercial property has many associated data points. Recalling an appropriate amount of context avoids information overload while covering historical dialogue parameters of user interest |
fileParseChunkSize | 800-1200 characters | Most commercial property operation documents are long tables. Chunk length adapts to structured data parsing to avoid incorrect field splitting |
globalVariableAutoUpdate | Enabled, refresh interval 3600 seconds | Core rent data for commercial properties updates monthly. The global variable refresh interval matches the core data update cycle to ensure timeliness of variables across sessions |
AI_NODE_OUTPUT_MODE | Return only structured results | Adapt to result extraction needs for engineering calls, avoiding redundant output during debugging |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Global variables are not consistent across multi-turn dialogue, and fields are empty when subsequent sessions are called. Cause:
globalVariableAutoUpdateis not enabled, or the correct refresh interval is not configured, causing variables to not update synchronously across sessions. - Phenomenon: After uploading commercial property operation documents, parsed results lack table fields. Cause:
fileParseChunkSizeis not adjusted to a length suitable for structured tables, causing long tables to be incorrectly split. - Phenomenon: Output content from the AI dialogue node includes debug information, making it impossible to directly extract business results. Cause:
AI_NODE_OUTPUT_MODEis not set to return only structured results; the default mode outputs conversational content.
How to Confirm Proper Configuration
- Initiate a multi-turn dialogue containing multiple sets of property data, verify that the system prompt automatically labels data sources and update cycles.
- Upload a commercial property operation table with multiple fields, check that the parsed fields are complete and have no splitting errors.
- Initiate two sessions at a specified interval, verify that the values of global variables change synchronously with the data update cycle.
- Call the AI dialogue node, check that the returned results only contain business data with no additional debug output.
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.