As energy infrastructure evolves with the integration of renewables and digital operations, the need for intelligent and automated inspection systems is more critical than ever. This session explores how Multi-Modal Generative AI-combining vision and language models-can be applied to transform raw inspection data (images + text) into structured, actionable maintenance reports. 

Participants will learn to build a GenAI-powered inspection assistant that analyzes images (e.g., solar panel defects, pipeline anomalies) and corresponding technician notes to generate human-readable reports. The session bridges computer vision, natural language processing, and domain-specific prompts to automate tasks traditionally done by expert operators, thus enhancing safety, efficiency, and compliance in the energy sector.

This is a hands-on session with synthetic data and open-source tools to empower participants to prototype and deploy multi-modal GenAI solutions

Key Technologies & Tools

• Image Captioning: BLIP, MiniGPT-4, Gemini Vision API, or HuggingFace Vision-Language models 
  
• LLM Prompting: OpenAI GPT-4 / LLama 3 + LangChain 
  
• Vector Store (optional): FAISS or Chroma if incorporating image metadata or prior records 
• Frontend UI: Streamlit or Gradio 
  
• Data Format: Drone/CCTV image samples + text logs in CSV/JSON