Internal Office and Employee Services for Land Pre-examination and Planning Permit: Multi-turn Dialogue and Prompt Engineering

Internal Office and Employee Services for Land Pre-examination and Planning Permit: Multi-turn Dialogue and Prompt Engineering

What the data for this category looks like

Data sources for this category include the national land spatial planning one-map, project approval and supervision platform, current land use database, and ecological protection red line data. Update frequency is irregular. Daily generated documents include land pre-examination and site selection opinions, construction land planning permits, construction project planning permits, red line maps, and planning conditions. Document structures include fields such as project number, site selection scope, land use scale, and control requirements. Most field units are hectares and square meters. Some documents include vector layer data.

Constraints on multi-turn dialogue and prompt engineering

Scattered data sources and irregular update cycles require multi-turn dialogue to pull the latest land spatial planning layer and approval data in real time. This avoids using expired information. Documents include vector layers and structured fields. Prompt engineering must distinguish between text retrieval and spatial coordinate verification scenarios. It must guide users to supplement necessary project information. Land pre-examination involves coordination of preconditions across multiple departments including development and reform, housing and urban-rural development, and ecological protection. Multi-turn dialogue must gradually guide users to supplement materials. This avoids interrupting interactions by requesting too much content at once. In internal office scenarios, employees need to quickly check conflicts between project site selection and planning control requirements. Prompt engineering must preset common approval risk points to assist rapid troubleshooting.

Configuration Settings

Reference only; verify against the target FastGPT release and deployment documentation.

Configuration ItemRecommended ValueRationale
Chunk Length800–1200 charactersDocuments related to land pre-examination include planning conditions and red line map text. This length fully covers a single control requirement or plot scope description
Overlap Length150–200 charactersAvoid splitting cross-chunk control requirements, and ensure retrieved content remains consistent during multi-turn dialogue
Single Search LimitTop 6 resultsMatch multi-dimensional control rules for land pre-examination verification, covering main approval key points
Maximum Context12000 tokenAdapt to project approval history and preliminary material information that require review during multi-turn dialogue
variables BindingProject number, land use scale fieldsAutomatically fill structured data in conversations, reducing manual input
messages Context RetentionLast 10 conversation turnsAligns with internal office scenarios where employees review multi-round approval communications

Three Common Misconfigurations

  • A 422 status code is returned when calling the dialogue interface. The cause is failure to bind exclusive variables parameters such as project number and land use scale. The interface cannot match the currently processed land pre-examination project data.
  • Duplicate planning control clauses appear in multi-turn dialogue search results. The cause is unreasonable chunk length settings, which split a single control requirement into multiple independent chunks, and no overlap length is configured to associate context.
  • The new conversation pop-up window cannot be hidden and cannot be modified via conventional configuration. The cause is direct modification of front-end source code without following private deployment code specifications, and failure to locate the entry file for the corresponding component.

How to Confirm Proper Configuration

  • Initiate a test conversation that includes a project number, and check if variables parameters are automatically filled into the conversation context.
  • Enter a query that includes red line scope, and verify that search results cover complete control requirements, with no duplicate or truncated content.
  • Simulate multi-round approval communications, and confirm that the number of retained conversation history matches the preset configuration, and no context overflow occurs.
  • Verify the code modification entry in the private deployment environment, and confirm that configuration items for the new conversation pop-up window can be adjusted normally.

The values provided are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.