Generative AI Training for Developers Overview
This Introduction Generative AI (GenAI) training teaches developers how to build intelligent, scalable applications using GenAI and large language models (LLMs).
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Understand the architecture and capabilities of Large Language Models (LLMs)
- Integrate LLMs into applications using popular frameworks like LangChain, OpenAI, HuggingFace, and LlamaIndex
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
Outline
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Introduction
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
Conclusion
Training Materials
All Generative AI training students receive comprehensive courseware.
Software Requirements
All attendees must have a modern web browser and an Internet connection.
