COURSE OUTLINE

Week 1: Foundations, Prompting, and Architecture

Day 1: AI and Generative AI Foundations

• AI Ecosystem Overview: Navigating the shift from Predictive to Generative AI.
• Deep Learning Fundamentals: Neural networks, backpropagation, and the rise of the Transformer.
• Transformer Architectures: Analyzing Attention mechanisms, Encoders, and Decoders.
• LLM Fundamentals: Understanding pre-training, SFT, and RLHF.

Day 2: Prompt Engineering Mastery

• Advanced Strategies: Chain-of-Thought (CoT), Tree-of-Thought, and Reasoning-and-Acting (ReAct).
• Optimization: Iterative prompt refinement and automated prompt tuning.
• Structured Prompting: Forcing outputs into JSON, XML, or specific schema formats.
• Context Engineering: Managing "Lost in the Middle" phenomena in long-context windows.

Day 3: Generative AI APIs and Integrations

• AI APIs Overview: Deep dive into the Vertex AI Gemini API and Google AI Studio.
• Integration Patterns: Synchronous vs. Asynchronous calls and streaming responses.
• AI Application Architecture: Decoupling the LLM layer from the application logic.
• Security: Managing IAM roles, API keys, and VPC Service Controls.

Day 4: Natural Language Processing (NLP) Concepts

• NLP Fundamentals: Tokenization, stop-word removal, and N-grams in the age of LLMs.
• Embeddings: Transforming text into high-dimensional vector representations.
• Vector Representations: Understanding cosine similarity and Euclidean distance.
• Semantic Search: Building search engines that understand intent rather than just keywords.

Day 5: Building AI Chatbots and Assistants

• Conversational AI: Designing dialogue flows and personality personas.
• Memory Management: Implementing short-term buffer memory vs. long-term database storage.
• Context Handling: Managing multi-turn conversations without losing the "thread."
• Assistant Development: Integrating Gemini with front-end frameworks (React/Python Streamlit).

Week 2: Advanced Implementation, Security, and Capstone

Day 6: Retrieval-Augmented Generation (RAG)

• Vector Databases: Deploying Vertex AI Vector Search (formerly Matching Engine) and Pinecone.
• Retrieval Techniques: Top-K retrieval, reranking, and hybrid search strategies.
• Knowledge Augmentation: Connecting models to live Google Search or internal BigQuery data.
• Enterprise Search: Solving the problem of "Hallucinations" through factual grounding.

Day 7: Fine-Tuning and Model Customization

• Fine-Tuning Concepts: When to fine-tune vs. when to use RAG.
• Transfer Learning: Leveraging pre-trained weights for specialized tasks.
• Parameter-Efficient Tuning (PEFT): Mastering LoRA (Low-Rank Adaptation) and QLoRA to save compute.
• Evaluation Methods: Using BLEU, ROUGE, and LLM-as-a-Judge for quality assurance.

Day 8: Multi-Modal AI and AI Agents

• Multimodal Generation: Generating assets with Imagen 3 (Images) and Veo (Video).
• AI Agents Architecture: Designing models that can "plan" and "execute" autonomously.
• Workflow Automation: Using Function Calling to trigger external Python scripts or APIs.
• Tool Integrations: Connecting agents to Google Calendar, Gmail, and Slack.

Day 9: AI Security, Ethics, and Governance

• Security Risks: Defending against Prompt Injection, Data Poisoning, and Jailbreaking.
• Responsible AI Frameworks: Implementing Google’s AI Principles in a production pipeline.
• Compliance: Navigating the EU AI Act and NIST risk management standards.
• Trustworthy AI: Implementing SynthID for watermarking and content safety filters.

Day 10: Capstone Project and Assessments

• End-to-End Solution: Developing a production-ready AI agent or RAG system.
• Team Implementation: Collaborating in a "sprint" environment to polish the MVP.
• Presentation: Technical defense of the architecture and ethical considerations.
• Roadmap: Career guidance for AI Architects and the future of Agentic Workflows.