Effective Prompting Guide
Learn how to craft effective prompts to get the best results from GeoRetina AI (GRAI). Understanding what the platform can and cannot do will help you phrase your requests for optimal outcomes.
The ROI-Based Workflow
GRAI operates on a Region of Interest (ROI) based workflow. This means all spatial analyses are performed on predefined areas that you draw or import, not on text-based location descriptions.
How to Work with ROIs
1. Create Your ROI
Draw a polygon on the map or import a shapefile to define your area of interest. Give it a descriptive name like "downtown_portland" or "amazon_study_area".
2. Reference in Prompts
Use the @ symbol followed by your ROI name in your prompts: "@downtown_portland" or "@amazon_study_area".
3. Analysis Execution
The platform analyzes only the area within your defined ROI boundaries, ensuring precise and focused results.
4. Mention Menu
When typing @, a mention menu appears showing all your available ROIs. Select the appropriate one for your analysis.
Understanding Platform Capabilities
GRAI specializes in geospatial analysis and querying. Before crafting your prompts, it's important to understand the platform's core strengths and limitations.
What GRAI Excels At
✓ Raster-Based Analysis
Satellite imagery analysis, land cover mapping, change detection, vegetation monitoring, weather forecasting, and environmental monitoring.
✓ Vector Data Querying
Spatial queries, proximity analysis, filtering by attributes, and combining multiple datasets.
✓ Time-Series Analysis
Multi-year trends, seasonal patterns, change detection, and temporal comparisons.
✓ Multi-Modal Integration
Combining raster and vector analysis, custom data integration, and progressive workflows.
What GRAI Cannot Do
Important Limitations
GRAI is a specialized geospatial platform. It cannot perform general data analysis, create non-geospatial visualizations, or handle analyses outside its supported capabilities.
✗ Non-Geospatial Analysis
Financial analysis, social media analytics, text processing unrelated to geography.
✗ Unsupported Data Types
Non-spatial databases, proprietary formats not listed in capabilities, real-time streaming data.
✗ Advanced GIS Operations
Complex spatial modeling, advanced geoprocessing workflows, custom algorithm development.
✗ Real-Time Analysis
Live satellite feeds, streaming IoT data, instant change detection.
Crafting Effective Prompts
1. Be Specific About Location
Always work with ROIs (Regions of Interest) that you've drawn or imported. GRAI analyzes the selected ROI, not text-based location descriptions.
How ROIs Work
Before making analysis requests, you must first draw a polygon on the map or import a shapefile to define your Region of Interest (ROI). Then select your ROI from the mention menu (@ROI_name) when crafting your analysis request.
✓ Good Examples (with ROI workflow)
"Generate a land cover map for @downtown_portland"
"Analyze urban heat island effects in @phoenix_metro"
"Show vegetation changes in @amazon_study_area from 2018 to 2023"
✗ Common Mistakes
"Show me land cover in New York" - Missing ROI reference, no time period
"Analyze heat islands" - No ROI selected, vague request
"Forest changes in Brazil" - Text location instead of ROI, missing timeframe
2. Specify Time Periods Clearly
For time-series analysis, be explicit about your temporal requirements.
✓ Clear Temporal Requests with ROIs
"Compare land cover between summer 2020 and summer 2023 in @study_area"
"Show urban heat trends from 2018 to present in @city_boundary"
"Vegetation monitoring from March to September 2023 in @agricultural_zone"
"10-day weather forecast for @project_area"
3. Use Supported Analysis Types
Frame your requests using the platform's available capabilities.
Raster Analysis Prompting
Land Cover Analysis
Perfect Prompts with ROIs
"Generate a land use/land cover map for @manhattan_area for summer 2024"
"Show me land cover classification for @agricultural_region"
"I need a LULC map of @coastal_study_area using the latest data"
Available Categories
Change Detection Analysis
Effective Change Detection Prompts
"Compare land cover changes between 2018 and 2022 in @deforestation_area"
"Show urban expansion from 2015 to 2023 in @jakarta_metro"
"Analyze agricultural land conversion over the past 5 years in @farming_district"
"Detect forest loss in @amazon_reserve between 2020 and 2024"
Urban Heat Island Analysis
UHI Analysis Prompts
"Analyze urban heat island effects in @downtown_la since 2018"
"Show temperature trends in @mumbai_districts"
"Compare summer heat patterns in @urban_suburban_zones"
"Track UHI intensity changes over the past 5 years in @city_boundary"
Vegetation Monitoring
Vegetation Analysis Prompts
"Monitor NDVI changes in @agricultural_fields from March to October 2023"
"Show vegetation health trends in @central_park over the growing season"
"Analyze forest health using EVI in @protected_reserve"
"Track crop development throughout the 2023 growing season in @farm_parcels"
Weather Forecasting
Weather Forecast Prompts
"Show me the 10-day weather forecast for @study_region"
"What's the precipitation forecast for @project_area for the next week?"
"Display temperature and wind patterns for @monitoring_zone for the next 5 days"
"I need weather data for @agricultural_zone for planning - next 7 days"
Air Pollution Analysis
Air Quality Prompts
"Analyze NO₂ concentrations in @beijing_area for the past month"
"Show ozone levels in @industrial_zone over the summer"
"Track air pollution trends in @mexico_city_boundary since January"
"Monitor SO₂ emissions in @power_plant_vicinity"
Vector Analysis Prompting
Proximity Queries
Distance-Based Queries
"Show all schools within 2km of @residential_district"
"Find hospitals within 5 miles of @downtown_area"
"List all restaurants within walking distance (500m) of @hotel_location"
"Show public transportation stops within 1km of @project_site"
Attribute-Based Filtering
Property-Based Queries
"Show all hospitals in @city_center"
"Find all restaurants with outdoor seating in @downtown_area"
"List all schools of type 'secondary' in @school_district"
"Show all highways classified as 'motorway' in @region"
Multi-Step Query Loops
Progressive Analysis
Step 1:
"Show all commercial buildings in @downtown_seattle"
Step 2:
"Of these buildings, show only those built after 2010"
Step 3:
"Among these newer buildings, which ones have LEED certification?"
Advanced Integration Prompting
Custom Data Integration
Working with Your Data
"Analyze land cover data for @property_boundaries"
"Show census data within @custom_districts"
"Find public amenities near @survey_points"
"Compare vegetation indices for @field_study_sites"
Knowledge Base Integration
RAG-Enhanced Queries
"Using our environmental impact reports, analyze land cover for @project_area"
"Based on our planning documents, show relevant infrastructure near @development_site"
"Reference our ecological studies and analyze current vegetation patterns in @study_zones"
Common Prompting Mistakes
Avoid These Patterns
❌ Common Mistakes
Requesting Unsupported Analysis Types:
"Calculate population density from satellite imagery" (not supported)
→ The platform cannot derive demographic data from imagery alone
Missing ROI References:
"Show recent changes in New York" (missing ROI)
→ Use ROIs: "Show recent changes in @nyc_study_area from 2020 to 2023"
Non-Geospatial Requests:
"Analyze stock market trends" (not geospatial)
→ Focus on geographic and spatial analysis only
Overly Complex Single Requests:
"Show land cover, calculate population, find schools, and predict weather all at once" (too complex)
→ Break into focused, sequential queries with proper ROI references
Pro Tips for Better Results
💡 Start Simple, Build Complex
Begin with basic analysis, then use follow-up questions to dive deeper. The platform maintains context between related queries.
🎯 Be Goal-Oriented
State your objective clearly: "I need to assess deforestation for a conservation report" helps the platform understand context.
🔄 Use Iterative Refinement
Refine your analysis progressively. Start broad, then narrow down based on initial results.
📊 Leverage Interactive Features
After getting results, use interactive querying tools to extract specific values and generate detailed charts.
Quick Reference: Prompt Templates
Raster Analysis Templates
Land Cover: "Generate a [land cover/LULC] map for @[roi_name] for [time period]"
Change Detection: "Compare [analysis type] between [start period] and [end period] in @[roi_name]"
Time Series: "Show [parameter] trends in @[roi_name] from [start] to [end]"
Weather: "Display [weather parameters] forecast for @[roi_name] for [time period]"
Environmental: "Analyze [pollutant/vegetation index] in @[roi_name] for [time period]"
Vector Analysis Templates
Proximity: "Show all [feature type] within [distance] of @[roi_name]"
Filtering: "Find [feature type] with [attribute condition] in @[roi_name]"
Counting: "Count the number of [features] in @[roi_name] that meet [criteria]"
Multi-step: "Of the previous results, show only those that [additional criteria]"
Remember: GRAI is most effective when you work within its specialized geospatial capabilities. Clear, specific prompts that align with the platform's strengths will yield the best results for your analysis needs.