AI for Energy
Go from AI-curious to AI-capable — and walk out knowing exactly where generative AI changes your work in the energy sector.
A hands-on, no-code introduction to generative AI for energy-sector professionals. You cover the foundations you need to make the tools intelligible, then move immediately to practical exercises: prompting, document retrieval with your own files, autonomous agents, and no-code workflow automation. The energy angle is grounded throughout — from use-case mapping across grid operations, predictive maintenance, renewables, and customer automation, to drills that apply directly to the data and documents your team actually uses.
Who it's for — Energy-sector professionals in operations, asset management, customer service, field engineering, or strategy — with no coding background — who want to use AI to do their existing job dramatically better.
- Explain how generative AI models are built and why they sometimes get things wrong — just enough to use them confidently
- Prompt effectively across any chat assistant and get consistently better output
- Make AI work with your own documents and data using RAG — no coding, no infrastructure
- Run autonomous web research tasks with a browser agent (nanobrowser)
- Build a no-code AI automation workflow that handles email or document tasks
- Map the highest-impact AI applications in the energy sector to your own role and prioritize where to start
You leave with A live browser agent and a no-code email/document automation you build hands-on.
These aren't demos — they're working agents you build hands-on and tailor to your own role, data, and custom spec.
Autonomous Browser Agent (nanobrowser)
A free Chrome-extension agent connected to a local or cloud model that runs autonomous web research tasks—useful for regulatory monitoring and market scanning—with no API keys or code.
Email Automation (n8n)
A no-code workflow triggered by incoming mail that drafts AI responses and runs sentiment analysis, aimed at high-volume customer-operations inboxes.
Document Q&A Automation
A RAG workflow that indexes company documents and answers customer or field queries against that corpus.
How you build them, class by class
How Generative AI Works
- The AI landscape: AI → Machine Learning → Neural Networks → Deep Learning → Generative AI
- Neural networks as large learned functions; what model parameters (weights) actually are
- Convolutional NNs (images) and Recurrent NNs (sequential data / time series)
- Generative Pretrained Transformers: architecture, training, inference, and why models hallucinate
Chat Assistants and Prompting
- The leading assistants — ChatGPT, Claude, Gemini, Meta AI, DeepSeek, Perplexity — and when to use each
- User prompting principles: role, context, task, format, constraints
- In-class exercise: apply prompting principles to a topic from your own work
- Weak vs. strong prompts — and the difference in output quality
Document Retrieval (RAG)
- What RAG is: Retrieve → Augment → Generate
- Why it matters for energy: field reports, specs, maintenance logs, regulatory filings were never in any model's training data
- In-class exercise: upload a document and interrogate it with questions
- The stack from single-document chat to enterprise RAG connected to internal systems
AI Agents and Automation
- What makes an agent different from a chatbot: plan → use tools → observe → loop
- In-class exercise: nanobrowser autonomous web research agent (free Chrome extension, no code)
- No-code automation platforms: Zapier, Make, n8n
- Email automation and document automation built with n8n and generative AI
AI in the Energy Sector
- Highest-impact applications today: grid optimization, predictive maintenance, renewable integration, customer automation, reservoir simulation, dynamic pricing
- AI-generated sector research as a research method — how to use Claude and Perplexity to build your own landscape
- Discussion: where AI will change the energy sector in 10 years
- Identifying your own priority use cases
Your hands-on capstone: a personal AI action plan for your role in the energy sector — a working prompt library, a RAG session on a document from your work, a configured browser-agent task, and a no-code automation workflow. It's the practical evidence your certificate is based on, and a toolkit you can use the next morning.
Your certificate is based on what you build — not attendance.
Ready to build your AI agent?
A small, high-quality cohort for leaders — fully online, hands-on from minute one.