COURSE OUTLINE

Day 1: Foundations of Generative AI for Developers

Focus: Understanding the "How" and "Why" behind the models.

• Evolutionary Context: Introduction to AI, ML, Deep Learning, and the jump to Generative AI.
• Deep Dive into LLMs: The Transformer architecture (Attention mechanisms, Encoders, and Decoders).
• The Developer’s Vocabulary: Master concepts like tokens, embeddings, and context windows.
• Ecosystem Overview: Navigating closed vs. open-source models (GPT-4 vs. Llama).
• Setup: Configuring IDEs (VS Code/Cursor) and AI coding assistants (GitHub Copilot).
• Hands-on: Benchmarking different models for specific coding tasks.

Day 2: Prompt Engineering & AI-Assisted Development

Focus: Turning AI into a high-performance pair programmer.

• Advanced Prompting: Zero-shot, one-shot, and few-shot learning strategies.
• Reasoning Frameworks: Using Chain-of-Thought (CoT) to solve complex logic puzzles.
• Development Lifecycle:
• Generation: Writing boilerplate and logic from scratch.
• Refactoring: Modernizing legacy code and optimizing performance.
• Documentation: Auto-generating docstrings and READMEs.
• Debugging: Using AI to interpret stack traces and suggest fixes.
• Hands-on Labs: Refactoring a monolithic script into modular, documented microservices.

Day 3: API Integration & Application Development

Focus: Programming with LLMs via APIs.

• The AI Stack: Introduction to RESTful AI APIs and SDKs.
• Security First: Managing API keys, rate limits, and authentication.
• Function Calling: Teaching models to interact with your own functions and external databases.
• State Management: Handling conversation history and context management in chatbots.
• Hands-on: Building a CLI tool or a web-based AI assistant that executes real code.

Day 4: Advanced Generative AI Development

Focus: Building smarter systems with memory and tools.

• Retrieval-Augmented Generation (RAG): Connecting models to external data sources.
• Vector Infrastructure: Understanding vector databases (Pinecone, Milvus, or Weaviate).
• Fine-tuning vs. RAG: When to retrain a model versus when to provide better context.
• AI Agents: Designing autonomous workflows that can plan and execute multi-step tasks.
• Multi-modal: Brief overview of integrating vision and audio models into apps.
• Hands-on Project: Implementing a RAG system that "chats" with a repository of local PDF documentation.

Day 5: Security, Governance, and Capstone

Focus: Shipping responsible and reliable AI.

• The Dark Side: Identifying Prompt Injection, data leakage, and insecure output handling.
• Reliability: Managing and reducing hallucinations through validation layers.
• Compliance: Navigating GDPR, privacy concerns, and responsible AI practices.
• The Future: Agents, "Small" Language Models (SLMs), and on-device AI.
• Capstone Implementation: Final development time for the integrated project.
• Presentations: Demonstrating the Capstone project and peer evaluation.