Google Cloud’s “Inspect Rich Documents with Gemini Multimodality and Multimodal RAG” skill badge explores using Gemini’s multimodal capabilities to extract insights from documents containing text, images, and video.
What I Learned
1. Multimodal Prompting
- Prompted Gemini with textual instructions, embedded visual data, and combined inputs for summaries and insight extraction.
- Gemini can interpret documents holistically, not just line-by-line.
2. Video Understanding
- Used Gemini to describe video content, extract info, and provide context-aware interpretations beyond transcripts.
3. Retrieval-Augmented Generation (RAG)
- Built a Multimodal RAG pipeline: extracted metadata, segmented text, queried Gemini, and cited sources for grounded, traceable answers.
Why It Matters
- Real-world data is rich and layered (legal, medical, visual docs)
- Learn to ask smarter questions, extract deeper meaning, and cite with confidence using GenAI
Whether you’re building AI assistants, enterprise tools, or working in information-heavy domains, this badge is a must-have.
Final Thoughts
This course helped me move from prompt experimentation to document intelligence. If you’re curious about the future of context-aware AI systems, dive into this badge.
#GenAIExchange #GoogleCloud #Gemini #MultimodalAI #RAG #AIForDocs #GenerativeAI #LLM