Section outline

• Select section Day 1: AI Foundations and Generative AI

  Day 1: AI Foundations and Generative AI

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  • Model Basics and Neural Networks
    • Linear regression and neural network fundamentals.
    • Lab: Create, train, and save a linear regression model.
  • Understanding Generative AI
    • AI vs ML vs Deep Learning vs Generative AI.
    • Overview of ChatGPT and LLMs.
    • Lab: Using OpenAI API for various tasks.
• Select section Day 2: Advanced Prompt Engineering and LangChain Basics

  Day 2: Advanced Prompt Engineering and LangChain Basics

  • Prompt Engineering
    • Techniques: Zero, one, few-shot, and chain-of-thought prompting.
    • Strategies: Self-feedback, critique, and iterative refinement.
    • Lab: Practical application of prompt engineering techniques.
  • LangChain Introduction
    • AI application architecture and LangChain essentials.
    • Memory usage, chaining, and LangSmith for tracing.
    • Lab: Build and use chains with LangChain.
• Select section Day 3: Embeddings, RAG, and AI Tools

  Day 3: Embeddings, RAG, and AI Tools

  • Embeddings, Vector Stores, and RAG
    • Embedding flows and visualizations.
    • Use ChromaDB and Pinecone for vector storage.
    • Lab: Create a retriever chain and implement RAG in an application.
  • Tools and Agents
    • Tool configuration for databases and web searches.
    • Agent execution and handling errors.
    • Lab: Build an app with tools and agents integration.