Professional Geo-AI & Prompt Engineering Masterclass

Now loading...

Professional Geo-AI & Prompt Engineering Masterclass

Sessions Venue
Online Apply Now

Benefits to join Boot Camp

Become a wise trader

Unlock amazing income source

Upgraded enablers community

3 months support

Module Breakdown

Now…here’s something SUPER EXCITING that we have to share with you…

This course helps participants develop practical skills in Geo-AI applications and prompt engineering, enabling them to make informed decisions while working with geospatial data and AI tools. The specially designed masterclass empowers learners to intelligently use AI models, geospatial technologies, and structured prompts to solve real-world spatial problems and enhance professional outcomes.

By the end of this course, participants will be able to decide and apply the following key aspects of Geo-AI and Prompt Engineering:

  • Identifying the right geospatial data sources and AI tools for specific use cases
  • Designing effective prompts to extract accurate, relevant, and actionable insights from AI models
  • Determining optimal workflows for spatial analysis, mapping, and AI-assisted decision making
  • Managing data scale, accuracy, and limitations in Geo-AI projects
  • Understanding operational processes and the use of digital platforms, GIS tools, and AI systems for geospatial analysis and automation
  • Prompting Fundamentals & Control: LLM Architecture, Professional Prompt Anatom (Role, Goal, Context), Zero-Shot vs. Few-Shot.
  • Structured Output & Constraint Enforcement: Using Delimiters, System Instructions, JSON Schema, XML, and Markdown for machine-readable output
  • Chain-of-Thought (CoT) Reasoning: Eliciting step-by-step logic, Zero-Shot CoT, and problem disambiguation
  • Advanced Reasoning Techniques: Tree-of-Thought (ToT) for parallel exploration and Self-Correction, Self-Consistency (SC) for consensus generation.
  • Retrieval Augmented Generation (RAG): RAG concept, Grounding with external data, and Synthesis of RAG documents.
  • ReAct & Tool Use Foundation: ReAct (Reasoning & Acting) loop, integrating LLMs with external APIs and real-world systems
  • Function Calling & Tool Definition: Defining functions for LLMs to accurately call specific GIS operations.
  • Multimodal Prompting: MLLM capabilities, image/text input integration for geospatial analysis, and interpreting satellite imagery.
  • Adversarial PE & Security: Prompt Injection, preventing goal hijacking, and security best practices for public-facing AI applications.
      Introduction (ArcGIS Pro & ArcPy)
    • ArcGIS Pro Fundamentals & Data Prep: Project creation, Coordinate Systems, Projections, and importing/exporting data (Shapefiles, Geodatabases)
    • Prompting for Code & Scripting: Generating functional ArcPy and SQL code, code explanation, and error identification for GIS development.
    • PE for Geo-Data Extraction: Generating structured GeoJSON from reports and normalizing messy address data using LLMs for geocoding.
      Mastering Vector Layers in ArcPy
    • Core Geoprocessing (ArcGIS Pro & ArcPy):Vector Analysis (Buffering, Clipping, Intersecting) and scripting fundamental tasks using ArcPy.
    • ArcGIS Online/Portal & Web Mapping: Publishing layers, creating Web Maps/Apps, and configuring visualization in the ArcGIS cloud environment.
    • Enterprise Database & Data Interoperability: Connecting ArcGIS Pro to PostGIS, performing Spatial SQL queries, and using enterprise data formats.
      Mastering Raster Layers in ArcPy
    • Raster Analysis (ArcGIS Pro & Spatial Analyst): Working with DEMs, Deriving Slope, Aspect, Hillshade, and Map Algebra.
    • 3D & Visualization (ArcGIS Pro 3D Analyst): Using ArcGIS Pro 3D Scenes and analyzing Point Cloud (Lidar) data with the 3D Analyst extension.
      Automating GIS Tasks with ArcPy
    • Automation (ModelBuilder & ArcPy): Creating repeatable workflows using ModelBuilder and advanced Python scripting.
    • Google Earth Engine (GEE) – JS Editor: GEE Architecture, filtering Image Collections (Sentinel, Landsat), and basic cloud-native visualization.
    • GEE Python API & Geo-ML: Setting up the GEE Python API for connecting to local environments and performing supervised classification.
      Advanced ArcPy Development
    • PE-GIS Integrated Project Planning: Final project scoping, data sourcing, and selecting appropriate PE and ArcGIS Pro tools for a comprehensive Geo-AI solution.
      Next Steps and Additional Resources
    • Capstone Project Presentation: Final project presentations, demonstration of the integrated workflow, and critique for professional advancement.

MOMENTS TO BE REMEMBERED