We're offering 20% off September Live Online classes! See which courses are applicable.   |   Details

AccountIcon BigDataIcon BlogIcon default_resource_icon CartIcon checkmark_icon cloud_devops_icon computer_network_admin_icon cyber_security_icon gsa_schedule_icon human_resources_icon location_icon phone_icon plus_icon programming_software_icon project_management_icon redhat_linux_icon search_icon sonography_icon sql_database_icon webinar_icon

Search UMBC Training Centers


Boosting Developer Productivity with AI Training

Group Training + View more dates & times


This AI training gives attendees a technical introduction to large language models (LLMs) and teaches them how to increase productivity with various AI tools, including ChatGPT and GitHub Copilot. In this Boosting Developer Productivity with AI course, participants learn how to use artificial intelligence (AI) to improve their efficiency, creativity, and problem-solving across diverse domains.


  • 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


2 days

Who Should Take This Course


  • Software developers
  • IT architects
  • Technical managers


Students should have an IT background or be interested in generative AI-driven programming.

Course Outline
  • Chapter 1 – 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
    • Summary
  • Chapter 2 – 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
    • Summary
  • Chapter 3 – 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
    • Summary
  • Chapter 4 – 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 …
    • Summary
  • Chapter 5 – 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
    • Summary

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!
Is there a discount available for current students?

UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.

What is the cancellation and refund policy?

Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

Contact Us