Multi-turn Dialogue and Prompt Engineering for Commercial

What the Data for This Category Looks Like

Commercial real estate investment research data comes from five main sources: project filing systems, rent collection ledgers, business district passenger flow monitoring platforms, industry association public reports, and government land transfer announcement information. Update frequencies vary across sources. Rent ledgers are updated monthly. Business district passenger flow data is updated weekly. Project filing and land transfer information is updated quarterly. Document structure falls into four categories:

Project basic information documents include fields such as project name, location, total construction area, and business format ratio.
Rent flow documents include fields such as lease term, daily rent per square meter, and collection status.
Business district competitor reports include content such as competitor project business formats, rent levels, and passenger flow peaks.
Passenger flow monitoring documents include data such as hourly passenger count and stay duration.

Field units follow industry standards. Construction area is measured in square meters. Rent unit price is measured in yuan per square meter per day. Passenger flow data is measured in passenger counts.

Constraints on Multi-turn Dialogue and Prompt Engineering

Differences in data update frequencies require multi-turn dialogue to support on-demand retrieval of the latest data. This avoids use of expired rent or passenger flow information. Documents can be lengthy. A single business district competitor report can reach several thousand characters. Multi-turn dialogue must support segmented retrieval and context stitching. This ensures full coverage of key information. There are diverse field dimensions and industry-specific terminology. Prompt engineering must clearly define retrieval scope and field formats. This avoids returning irrelevant data. Different data sources have varying levels of authority. For example, project filing data has higher priority than third-party reports. Multi-turn dialogue prompt engineering must specify data source priority rules. This ensures response credibility.

Configuration Recommendations

Configuration Item	Recommended Value Range	Rationale
`maxContext`	`8000–12000 characters`	Commercial real estate investment research documents can be several thousand characters long per piece. Multi-turn dialogue must retain multiple project reports, rent data, and competitor information to avoid context overflow
`recall_top_k`	`Top 6–8 entries`	Commercial real estate data dimensions cover four categories: projects, rent, competitors, and passenger flow. A sufficient number of entries must be retrieved to cover full-dimensional key information
`similarity_threshold`	`0.72–0.78`	Commercial real estate has a large number of industry-specific terms. Balance retrieval precision and coverage to avoid missing relevant investment research data
`global_variable_sync_mode`	`Real-time sync within session`	Commercial real estate rent data must take effect immediately after updates within the same session, ensuring subsequent questions can obtain the latest variable values
`dialogue_list_offset`	`Starts at 0, step size equals the number of dialogues already retrieved in the current session`	Matches the dialogue turn of the user's question to avoid duplicate or missed historical dialogue records
`dialogue_history_fetch_limit`	`3 entries per turn`	Corresponds to scenarios with three consecutive questions, ensuring each turn can obtain complete single-user question content

The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, 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: When calling the GET /api/dialogue/list interface, the returned dialogue records do not correspond to user questions and AI responses. Cause: The dialogue_list_offset parameter was not set with a step size equal to the number of dialogues already retrieved in the current session, resulting in offset misalignment.
Issue: The initial global variable value passed in multi-turn dialogue displays normally, but subsequent modifications do not take effect. Cause: The real-time sync within session configuration for global_variable_sync_mode was not enabled. The variable is only loaded once when the session starts.
Issue: Individual user questions cannot be retrieved during multi-turn dialogue. Cause: dialogue_history_fetch_limit was not configured to retrieve single turns. The default configuration only returns aggregated context and does not return single question content.

How to Verify Correct Configuration

Call the GET /api/dialogue/list interface, pass a known session ID and dialogue_list_offset=0, and verify that the question field of the first returned record matches the first user question.
Modify a global variable within a session, then submit a new question, and verify that the response includes the modified variable value.
Submit three consecutive questions, call the interface to retrieve dialogue records, and verify that the returned list includes three question fields corresponding to the submitted questions.
Set similarity_threshold=0.75, retrieve commercial real estate project rent data, and verify that the title and keyword matching of the retrieved results meets expectations.

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.