WA3417
Boosting Developer Productivity with AI Training
This Artificial Intelligence (AI) training gives developers a technical introduction to large language models (LLMs) and teaches them how to increase their coding productivity with various AI tools, including ChatGPT and GitHub Copilot.
Course Details
Duration
2 days
Prerequisites
- IT background or be interested in generative AI-driven programming
Target Audience
- Software developers
- IT architects
- Technical managers
Skills Gained
- Understand LLMs' fundamental concepts and principles
- Gain insights into the diverse applications of LLMs across various domains, including natural language processing, creative text generation, and code development
- Enhance productivity and problem-solving with AI
- Develop proficiency using popular LLM platforms and tools like OpenAI's ChatGPT and GitHub Copilot
- Explore ethical considerations and potential risks associated with LLM usage
- Apply LLM-powered techniques to practical scenarios
Course Outline
- Introduction to Large Language Models
- What is Generative AI?
- A Bit of History ...
- ... and Then ...
- RNNs
- Problems with RNNs
- Transformers
- Encoders and Decoders
- Generative AI and LLMs
- Training the Model to Predict the Next Word Visually
- The LLMs Landscape
- The Evolutionary Tree of LLMs
- The Microsoft 365 Copilot Ecosystem
- The LLM Capabilities vs LLM Size (in Parameters)
- Does the Model Size Matter?
- Inference Accuracy vs LLM Size
- Open AI GPT Models
- Llama
- The LLaMA Family of LLMs
- LLaMA 2
- The AI-Powered Chatbots
- How Can I Access LLMs?
- Options for Accessing LLMs
- Cloud Hosting
- Opinions about LLMs
- Multimodality of LLMs
- Infographic of Multimodality Tasks
- Example of an LLM Explaining a Joke
- Example of Cause & Effect Reasoning
- Inferring Movie from Emoji
- Prompt Engineering
- The Right People, with the Right Skills, for the Right Time ...
- Context Window and Prompts
- Zero- and Few-Shot Prompting
- The Training Datasets
- The RedPajama Project (OSS LLaMA Dataset)
- AI Alignment
- Reinforcement Learning with Human Feedback (RLHF)
- Problems with RLHF
- Ethical AI
- LLMs, a Technologist's Perspective
- LLM Operational Aspects
- Understanding Model Sizes
- Physical Model Sizes
- The Training and Inference Costs
- The Model Training Phase's Carbon Footprint
- Quantization
- Model Formats
- LLM Accuracy Benchmarks
- Open and Closed Book Benchmarks
- The Perplexity Performance Metric
- Embeddings
- Where are Embeddings Used?
- The Vector Databases
- LLM Concerns
- Ways to Interface with Local LLMs
- Using a Supported Programming API (Binding)
- UI Options
- Customization Options for LLMs
- Customization Options: Top-p and Top-k
- Customization Options: Temperature and Repetition Penalty
- Customization Option: The Turn Template
- Configuration Presets
- Introduction to ChatGPT
- A Stylized OpenAI ChatGPT Logo
- OpenAI GPT Models
- OpenAI Models
- ChatGPT 4.0
- ChatGPT Prompts
- ChatGPT Prompts Strategies, Tactics, and Best Practices
- Prompt Engineering: Dealing with ChatGPT's Hallucination Syndrome
- Prompt Engineering: Break Down the Complex Tasks into Smaller Ones
- Prompt Engineering: Examples of Prompts
- OpenAI API
- GPT Embeddings
- Embedding Models' Risks and Limitations
- OK. How Can I Get My OpenAI Embedding
- Tokens, Take 1
- Tokens, Take 2
- The Tokenizer UI
- Prompts, Embeddings, and Tokens
- AI-Powered Developer Productivity
- Generative AI and LLMs for Developers
- How to Become a Technologies and Philosopher All in One
- Gartner on AI-augmented Development Tools
- Developer-AI Pair Programming Paradigm
- The Tooling
- Some Facts ...
- Code Generation: SQL Example
- Code Generation: Using ThreadLocal Storage in Java
- Code Generation: Thread-safe Singleton Design Pattern in C#
- Code Generation: Bash Scripting
- Code-to-Code Translation
- Code Llama
- Fine-Tuning Llama 2 Workflows
- GitHub Copilot
- Can I Trust AI-Generated Code?
- The Safeguards
- The General Recommendations ...
- Introduction to GitHub Copilot
- What is GitHub Copilot?
- Copilot Chat
- IDE and REPL Integrations
- Will Copilot Replace Developers?
- Can I Trust Code Generated by GitHub Copilot Code?
- GitHub Copilot's Modus Operandi
- The Life of a Code Completion: The Big Picture
- Code Suggestions are Not Copy & Paste from Other Peoples' Code
- The Shebang Prologue Hint
- Getting Started with GitHub Copilot
- GitHub Copilot Plans
- Copilot for Individuals
- Copilot for Businesses
- GitHub Copilot Security
- Responsible Copilot
- Lab Exercises
- Lab 1. Learning the Colab Jupyter Notebook Environment
- Lab 2. Hello, AI!
- Lab 3. OpenAI Platform Overview
- Lab 4. Using OpenAI API
- Lab 5. Understanding Embeddings
- Lab 6. OpenAI API Project
- Lab 7. Copilot Environment Setup
- Lab 8. Hello, Copilot!
Upcoming Course Dates
