Google Gen AI Intensive Course

Learning Outcomes

• Foundation models & prompting: Explain core LLM concepts and apply prompt-engineering patterns.
• Embeddings & retrieval: Use embeddings and vector search to support RAG-style tasks.
• Agentic workflows: Design simple GenAI agents and reason about multi-step tool use.
• Domain-specific LLMs: Understand considerations for specialized models and their applications.
• Modeling Relationships: Built and explained linear regression models to analyze relationships between variables.
• GenAI MLOps: Outline deployment/monitoring practices tailored to generative AI systems.

Takeaways

This intensive program was one of the most fast-paced and information-dense learning experiences I have taken part in. Within just five days, I was immersed in the essential building blocks of generative AI, from understanding foundation models and prompting strategies to experimenting with embeddings, retrieval-augmented generation, and lightweight agentic workflows. What made the course particularly valuable was the balance between expert-led seminars and the practical Kaggle labs, where I could immediately apply theoretical concepts to real coding exercises. The capstone project was especially rewarding, as it pushed me to synthesize the different modules into an end-to-end solution, giving me first-hand experience in structuring a GenAI pipeline under time pressure. Through this process, I gained a stronger appreciation of the practical challenges in deploying generative systems, including evaluation and monitoring, which I had previously only read about. It reinforced the idea that mastering these tools requires not just technical proficiency, but also a thoughtful consideration of use cases, limitations, and domain adaptation. Overall, completing this program has given me a solid foundation to continue experimenting with generative AI and to integrate these methods into my future academic and professional projects.