Section outline

• Select section Day 1: Fundamentals and LLM Models

  Day 1: Fundamentals and LLM Models

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  • Foundations of AI and ML
    • Introduction to AI, neural networks, and foundation LLM models.
  • LLM Ecosystem
    • Tools, datasets, cloud platforms, and model comparisons (GPT-3, BERT, Llama).
  • Vector Databases
    • Introduction and applications in Generative AI.
    • Lab: Work with a vector database.
• Select section Day 2: Practical Implementation with LangChain

  Day 2: Practical Implementation with LangChain

  • LangChain and GPT Models
    • Prompts, templates, and sequential chains.
    • Lab: Interface with AI models using LangChain.
  • RAG and Chatbot Development
    • Techniques, data management, and deploying RAG-based LLMs.
    • Lab: Build and deploy LLM-powered chatbots.
• Select section Day 3: Advanced Techniques and Use Cases

  Day 3: Advanced Techniques and Use Cases

  • Summarization and Embeddings
    • Advanced strategies for summarization and embeddings.
    • Lab: Implement these techniques with LangChain.
  • Model Fine-Tuning
    • Techniques, constraints, and planning fine-tuning.
    • Lab: Fine-tune and validate models.
  • Use Cases
    • Document analysis, chatbots, workflow automation, and data analysis.