EE403 — Prompt Engineering and RAG for Energy

The Highest-ROI Course in the Curriculum

EE403 delivers more immediate practical value than any other course in GIEE's AI in Energy curriculum. Engineers can apply these techniques to their daily work within hours of completing the material. This is the course where AI stops being theoretical and becomes a tool you actually use.

You will master prompt engineering — the discipline of getting reliable, high-quality output from large language models like ChatGPT, Claude, and Copilot. You will then go beyond prompting to build Retrieval-Augmented Generation (RAG) systems that ground LLM responses in your organization's documents: NERC standards, internal procedures, equipment manuals, regulatory filings, technical reports.

By the end of this course, you will have built a working RAG system that answers technical questions from a real energy document corpus. You will leave with templates, frameworks, and code patterns you can adapt for your own organization the same week.

EE403 is required for both CAGP (Certified AI for Grid Professional) and CAEP (Certified AI for Energy Professional) certifications. It is also a Phase 1 priority course in GIEE's launch sequence, alongside EE400 (Fundamentals) and EE406 (Python AI Ecosystem).

What You Will Learn

• Apply the RCFT framework (Role, Context, Format, Task) to construct effective prompts for any LLM
• Use advanced prompting techniques: chain-of-thought, few-shot learning, tree-of-thought reasoning
• Design and build complete RAG systems from document ingestion through generation
• Choose between prompting, fine-tuning, and RAG based on use case requirements
• Implement document chunking, embedding, and vector search strategies appropriate to energy documents
• Build a production-ready RAG system with citations, source attribution, and answer evaluation
• Apply prompt engineering and RAG to real utility workflows: outage communications, regulatory analysis, equipment troubleshooting
• Evaluate RAG system quality using appropriate metrics and human review processes

Course Structure

EE403 is organized into four modules that progress from prompt engineering fundamentals through complete RAG system implementation:

• Module 1: Prompt Engineering Foundations — How LLMs work from a prompting perspective. The RCFT framework. Common patterns and anti-patterns. Energy-specific prompting examples.
• Module 2: Advanced Prompting Techniques — Chain-of-thought, few-shot, tree-of-thought, self-consistency. When each technique helps and when it does not. Hands-on with energy use cases.
• Module 3: RAG Architecture and Design — When RAG fits and when it does not. Document chunking strategies. Embedding models. Vector databases. Hybrid search. Citation and source attribution.
• Module 4: Building a Production RAG System — End-to-end build of a working RAG system on a real NERC standard. Testing, evaluation, deployment considerations, and the path from prototype to production.

Real-World Examples and Hands-On Build

This is the most hands-on Core course. You will work through real energy scenarios throughout: drafting outage communications for multiple audiences, analyzing regulatory documents for compliance impact, troubleshooting equipment issues using technical manuals, and processing field engineering reports.

The Module 4 capstone is a complete working RAG system. You will ingest a real NERC standard, build the document chunking and embedding pipeline, implement retrieval and generation, add citation handling, and evaluate the system's accuracy. You leave with code you can adapt for your own organization's documents.

Who This Course Is For

• Practicing engineers who want immediate productivity gains from AI tools
• Engineers tasked with building or evaluating LLM-based systems for their organization
• Technical writers, regulatory analysts, and compliance professionals working with energy documents
• AI champions building internal RAG systems for utilities or energy companies
• Engineering leaders evaluating RAG vendors and proposals
• Anyone pursuing CAGP or CAEP certification

Prerequisites

• EE400 — AI and Machine Learning Fundamentals for Energy (recommended; provides the AI vocabulary foundation)
• EE401 — Deep Learning, LLMs and Generative AI for Energy (helpful but not required; EE403 covers LLMs from a practical user perspective even if you have not taken EE401)
• Comfort with using LLMs at a basic level (you have used ChatGPT or similar before)
• Light Python familiarity helps with the Module 4 build but is not required at a development level

Format and Access

• Duration: Approximately 12 hours of content
• Format: Self-paced online with video instruction, demonstrations, and a hands-on RAG build
• Course Access: 6 months of full access from enrollment
• Completion Window: 90 days to complete coursework and the final exam
• Assessment: 4 module quizzes (30% of grade) + comprehensive final exam (70% of grade)
• Passing Score: 70% overall
• Language: English
• AI Tools: Encouraged for learning, exercises, and the hands-on build (the course is literally about AI tools); prohibited during quizzes and the final exam

Path to Certification

EE403 is the fourth course in GIEE's AI in Energy Core curriculum and contributes to two professional certifications:

• CAGP — Certified AI for Grid Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Grid Add-On courses (EE410, EE411, EE412), then pass the CAGP certification exam.
• CAEP — Certified AI for Energy Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Energy Add-On courses (EE420, EE421, EE422), then pass the CAEP certification exam.