WA3508
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
Duration
3 days
Prerequisites
- 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
- Gain a deep understanding of LLM fundamentals to make informed decisions for your organization's AI strategy
- Craft high-impact prompts to unlock the full power of LLMs and achieve precise results
- Bolster your development process with LLM-powered tools that streamline workflows and elevate code quality
- Confidently access and integrate both closed-source and open-source LLMs into your projects
- Customize LLMs for tailored solutions that drive innovation and efficiency
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
