Phoenix TS

Boosting Developer Productivity with AI Training

BONUS! Cyber Phoenix Subscription Included: All Phoenix TS students receive complimentary ninety (90) day access to the Cyber Phoenix learning platform, which hosts hundreds of expert asynchronous training courses in Cybersecurity, IT, Soft Skills, and Management and more!

Course Overview

This two – day, instructor led Artificial Intelligence (AI) training in Washington, DC Metro, Tysons Corner, VA, Columbia, MD or Live Online, 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. This course is intended for Software developers, IT architects and Technical managers. At the completion of this course, participants will be able to:

  • 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


Boosting Developer Productivity with AI Training

6/24/24 - 6/25/24 (2 days)
7/29/24 - 7/30/24 (2 days)
9/16/24 - 9/17/24 (2 days)


All learners are required to have:

  • IT background or be interested in generative AI-driven programming

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!

BONUS! Cyber Phoenix Subscription Included: All Phoenix TS students receive complimentary ninety (90) day access to the Cyber Phoenix learning platform, which hosts hundreds of expert asynchronous training courses in Cybersecurity, IT, Soft Skills, and Management and more!

Phoenix TS is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints re-garding registered sponsors may be submitted to the National Registry of CPE Sponsors through its web site: www.nasbaregistry.org

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