Generative AI Training for Developers Overview
This Comprehensive Generative AI training course explores the foundations and applications of generative AI (GenAI), starting with core AI/ML concepts and progressing through large language models (LLMs) and prompt engineering. Participants learn how to use natural language processing (NLP) techniques, build applications using LangChain, and implement retrieval augmented generation (RAG). The course also covers GenAI security concerns, LLMs via APIs, and advanced methods for diverse AI tasks.
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
- Define core concepts of artificial intelligence and machine learning
- Explain the mechanisms and types of large language models
- Develop effective prompt engineering strategies for various NLP tasks
- Implement retrieval augmented generation techniques for knowledge-based applications
- Build and scale LangChain applications for complex problem-solving
- Analyze security risks and defensive strategies for generative AI
- Integrate LLMs through APIs and function calling for diverse applications
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 to Generative AI
- What is Intelligence?
- Mechanisms of Intelligence
- What is Artificial Intelligence?
- How does AI work?
- Applications of AI
- Machine Learning and its Types
- Emergence of Generative AI
Understanding Large Language Models
- What is a Language Model?
- Natural Language Processing (NLP)
- Tokenization: Approaches and Strategies
- Word and Sentence Embeddings
- Types and Training of Large Language Models
- Applications and Value Proposition of LLMs
Accessing LLMs through API
- Closed Source vs. Open Source LLMs
- Accessing LLMs via API and Benefits
- Prompt Templates and Structures
- Function Calling and Integration with LangChain
Basics of Prompt Engineering
- The Art and Science of Prompt Engineering
- Communicating with LLMs
- Principles of Effective Prompts
- Common NLP Tasks and Strategies
Prompt Engineering for Engineers
- Introduction to Code Generation
- Effective Code Generation Techniques
- Advanced Prompt Engineering Methods
- Visualizing Chain of Thought
Introduction to Retrieval Augmented Generation
- Understanding RAG and its Advantages
- Vector Embeddings and Integrating with Knowledge Bases
- Document Indexing and Retrieval Techniques
- Managing Large Documents and Knowledge Bases
Advanced RAG Techniques
- Enhancing Retrieval Accuracy and Performance
- Handling Multilingual Retrieval and Error Management
- Query Construction and Mitigating "Lost in the Middle" Effect
LangChain Agents
- Introduction to LangChain Framework and Tools
- Building and Scaling LangChain Applications
- State Management and Multi-Tool Coordination
Generative AI Security and Privacy
- Importance and Potential Risks of GenAI Security
- Key Vulnerabilities and Attack Vectors
- Principles and Defensive Strategies for Security
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.
