Introduction to Generative AI for Developers Training

This Introduction Generative AI (GenAI) training teaches developers how to build intelligent, scalable applications using GenAI and large language models (LLMs).

Course Details


5 days


  • Practical experience in Python (at least 6 months):
    • Data Structures, Functions, Control Structures
    • Exception Handling, File I/O, async, concurrency (recommended)
  • Practical experience with these Python libraries: Pandas, NumPy, and scikit-learn
    • Understanding of Machine Learning concepts - regression, clustering, classification
    • ML Algorithms: Gradient Descent, Linear Regression
  • Loss Functions and evaluation metrics

Skills Gained

  • Understand the architecture and capabilities of Large Language Models (LLMs)
  • Integrate LLMs into applications using popular frameworks like LangChain, OpenAI, HuggingFace, and LlamaIndex
Course Outline
  • LLM Foundations
    • Introduction to Generative AI for Software Development
    • Generative Models and their Use Cases
    • Transformer architecture and its impact on LLM performance
    • LLM Training Process - pre-training, fine-tuning, and reinforcement learning
    • Exploring Real-World LLM Applications
  • Speaking to LLMs: Prompt Engineering
    • Prompt Engineering Introduction
    • Techniques for creating effective prompts
    • Zero-Shot Learning, Few-Shot, and Chain-of-Thought
    • Prompt Engineering for Developers
    • Leverage LLMs for code generation, completion, and analysis
    • Best practices for prompt design and optimization in a development context
    • Optimize prompting workflows for next-generation scripting
    • Handle and process LLM-generated code
    • Integrate prompts into development pipelines
  • Accessing LLMs via APIs
    • Accessing GPT 3.5 and GPT 4 via the OpenAI API
    • Roles and Conversation Threading
    • Popular LLMs, APIs, and Libraries - Generative AI Tech Stack
    • LangChain for Integration
    • Closed-Source LLMs vs Open-Source LLMs
    • Chat Agents for Querying Developer Documentation via API
  • Enhancing LLMs with Fine-Tuning
    • State of the Art Open-Source LLMs
    • Building Pipelines with HuggingFace Transformers Library
    • Fine-Tuning with the Hugging Face Transformers library and code-specific data