Using an LLM with Regrid API

Integrating the Regrid Parcel API into an LLM (Large Language Model) or AI Agent system requires a different architectural approach than standard web integrations. Because LLMs are non-deterministic and can "loop" or perform broad searches, unoptimized usage can lead to rapid credit depletion and unexpected overages.

This guide outlines best practices for developers building AI-driven property tools to ensure efficiency and cost-control.

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1. Understanding Usage in an LLM Context

Unlike a human user who clicks a single button, an LLM agent might interpret a prompt like "Find all properties owned by John Smith in Texas" by making dozens of API calls.

• Billing Metric: Regrid bills based on Parcel Records Returned, not just the number of API requests.
  • The LLM Risk: If an LLM is given a broad tool (e.g., query), it may inadvertently trigger a search that returns 500+ records (the maximum is 1,000 per request). If the agent does this across multiple counties, you could consume your entire monthly quota in minutes.
    • Default Behavior: By default, Regrid returns 20 records per request. If your LLM doesn't specify a limit, it will pull 20 records even if it only needs one, doubling or tripling your expected cost.
      
      

2. How Overages Occur (Self-Serve Plans)

Self-serve plans come with a pre-set number of monthly parcel records of 2,000 parcel records and 200,000 tiles however the Self-serve API is a hybrid model and overages start after the pre-set has been consumed.

• The "Above and Beyond" Factor: Once your monthly quota is met, the API does not necessarily hard-stop (depending on your plan configuration).
  • Automatic Top-offs: Many self-serve systems are designed to allow "overage buckets" to ensure your application doesn't break. If an LLM agent gets stuck in a logic loop—repeatedly querying the same area or expanding a search radius—it can trigger multiple overage charges before you receive a billing alert.
    • Broad Geometries: In LLM systems using spatial tools, an agent might draw a large bounding box or polygon. If that polygon covers a dense urban area, a single "search" could return hundreds of records, each counting against your bill.
  • Set Usage Limits Manually: Navigate to your Usage dashboard and change the limit usage to set a maximum spend per month. This will ensure that you are not hit with an unexpected high bill based on using the API in an LLM.
  • Usage Notifications: A usage notification is automatically sent when usage has reach 80% of 2,000 parcel records. This is your warning to review your usage. No change to your behavior will automatically start generating overages once the 2,000 parcel records have been surpassed.

3. Best Practices for LLM Implementation

A. Strict Token Engineering (The "Limit" Parameter)

Never give an LLM "blind" access to the API. Hard-code a limit parameter in your tool definition.

• Best Practice: Set a system-level limit=2 or limit=5 for general lookups.
  • Example Prompt for the Agent: "You are an assistant that finds property data. You must ALWAYS include limit=1 in your API call if the user is asking about a specific address."
    
    

B. Use the Usage Endpoint for Self-Correction

Integrate the Regrid Usage API Endpoint into your LLM’s "system prompt."

• Mechanism: Have the agent check its remaining balance before performing "expensive" operations (like polygon searches).
  • Agent Logic: "If remaining_balance < 1000, warn the user that broad searches are restricted."
    
    

C. Implement "Human-in-the-loop" for Large Queries

If the LLM determines it needs to pull more than 50 records to answer a question:

• Best Practice: Program the agent to stop and ask: "This search will return 450 parcel records. Do you wish to proceed?" This prevents automated "hallucinations" from draining your account.
  • Invoke the return_count parameter to check the number of parcel records the specific request would return to determine the decision
    
    

D. Spatial Constraints

LLMs are notoriously bad at estimating latitude/longitude coordinates.

• The Fix: Use a geocoding service (like Google or Mapbox) to convert addresses to precise points before calling Regrid. Use the Point-in-Polygon or Radius search with a very small radius (e.g., 10 meters) to ensure you only return the single, correct parcel.
  
  

4. Summary Checklist for Reducing Usage

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Developer Note: If you are building a high-traffic LLM application, contact Regrid at [email protected] to discuss an Enterprise License. Enterprise plans offer more predictable pricing and higher rate limits (standard is ~200 requests/min), which are often necessary for the "bursty" nature of AI agents.