What I learnt in the 5-Day Gen AI Intensive Course with Google

Source: Dev.to

I recently completed the 5‑Day Gen AI Intensive Course with Google. My motivation was two‑fold: a love for Python (sorry, TypeScript) and a fascination with AI agents. Below is a concise recap of what I learned and how it reshaped my existing agent projects.

Day 1 – Foundations & Prompting

• Background – I entered the course with several agent projects (Discord bots using RAG, a real‑time voice agent built on LiveKit + FastAPI, and a hackathon‑style customer‑support bot).
• Key Insight – My earlier implementations felt fragile; a single edge case could cause the whole system to collapse.
• Learning Highlights
  • Explored the NotebookLM whitepaper on foundational LLMs, which clarified why certain prompts succeeded or failed.
  • Completed the prompting fundamentals codelab and discovered the impact of temperature, top‑k, and top‑p on output randomness and creativity.

Day 2 – Evaluation & Structured Outputs

• Problem – I was manually inspecting agent responses, which was inefficient and error‑prone.
• Solution – Learned about autoraters and structured outputs, enabling programmatic verification of hallucinations and adherence to expected behavior.

Day 3 – Embeddings, Vector Stores & Retrieval‑Augmented Generation (RAG)

• Previous Experience – Used Chroma and other vector stores without fully understanding their quirks.
• Breakthrough – The embeddings whitepaper explained the theory behind vector similarity, helping me diagnose failed searches.
• Hands‑On – Built a document Q&A system from scratch, experimenting with:
  • Chunk sizing and overlap strategies
  • The direct relationship between embedding quality and retrieval performance

Day 4 – Multi‑Agent Systems & State Management

• Exploration – Delved into multi‑agent architectures, inspired by the Gemini “AI‑using‑AI” approach.
• Core Concepts
  • State machines and agent loops for managing multi‑turn conversations
  • Techniques to preserve context across interactions without degradation

Day 5 – MLOps & Deployment Considerations

• Focus – The final session covered operational aspects (MLOps, DevOps, DataOps) and the challenges of scaling agents in production.
• Takeaway – While there was no hands‑on codelab, the discussion highlighted the importance of sandbox environments for safe deployment and monitoring.

Final Thoughts

AI agents are becoming increasingly integral to everyday workflows. This intensive course filled many of my knowledge gaps—from low‑level model mechanics to high‑level system design—and gave me practical tools to build more robust, reliable agents. I look forward to applying these insights to future projects.