AI
AI Empowered Software Development
AI Empowered Software Development is an intensive three day hands on course exploring ways in which experienced software developers and teams can enhance their productivity dramatically through the use of modern AI tools including generative AI systems, coding assistants, co-pilots, development environment addons, simulated data generation tools, testing tools, deployment tools and collaboration tools – while maintaining code accuracy, security and responsibility.
This course covers foundational through advanced concepts in AI code assistance and prompt engineering using a wide range of tools. Numerous labs featuring Visual Studio Code and GitHub CoPilot. The course also includes coverage of generative AI based tools such as ChatGPT, Gemini, Claude, Perplexity and more, in the context of cross analysis, ideation, architecture design and best practices auditing.
Target Languages
While generally applicable to development in any programming language, this course is typically delivered biased toward one programming language and all of the lab work is coded in this “target language”. Target languages available:
- Python
- JavaScript
- Go
- Rust
- Java
- C#
- C++
- C
Duration
3 days
Audience
Anyone interested in generative AI accelerated software engineering.
Prerequisites
- Proficiency in target language
- Basic SQL skills, as well as
- Experience with basic software development workflows
- Familiarity with typical development tools like Git, Docker, and IDEs
Upon completing this course, participants will be able to:
- Demonstrate mastery of AI tools and techniques to enhance software development productivity
- Show proficiency in applying the target language and SQL with AI-powered assistants
- Use Generative AI to augment critical thinking around code structure and architecture
- Work with AI in problem-solving settings
- Understand the basis for hallucinations and how to reduce them
- Have confidence using AI for testing, deployment, and collaboration
- Identify ethical and responsible AI development practices
- Create high-quality documents, articles and marketing content
Day 1: Foundations and Core Development Practices
- Introduction to AI in Software Development
- Overview of AI’s role in modern software development
- Generative AI, Large Language Models (LLMs), co-pilots, and AI-powered tools
- Key benefits and challenges for developers adopting AI
- Examples of AI-powered tools (e.g., code assistants, testing frameworks, analytics engines)
- Integration of AI within the Software Development Life Cycle (SDLC)
- LAB: Lab environment setup and exploration
- AI-Powered Coding Assistants and Development Environments
- Demonstration of coding assistants (e.g., Visual Studio Code, JetBrains IDEs, GitHub Copilot, Tabnine)
- Configuring IDEs and development tools to integrate AI solutions
- Customizing IDEs with AI plugins
- Utilizing AI for auto-completing, generating code snippets, and reducing boilerplate
- LAB: Writing code with an AI coding assistant
- Advanced Coding Assistant Usage
- Enhancing code documentation and maintaining consistency with AI
- Evaluating AI-generated code quality
- AI code reviews
- Context and selective context
- LAB: Using advanced code assistant features
- Data Generation and Preprocessing with AI
- Introduction to simulated data generation tools (e.g., Faker, GPT-generated datasets)
- Responsible handling of synthetic data for testing
- LAB: Generating sample datasets software projects
Day 2: AI in CI/CD, Testing and Deployment
- AI-Assisted Code Testing and QA
- Introduction to AI-powered testing tools (e.g. Cody, Greptile, Codacy, DeepSource)
- AI for automated debugging, unit testing, api testing, web testing, chaos testing and more
- AI-powered error detection and code quality optimization
- Automating code linting and performance checks
- LAB: Writing and optimizing test cases
- AI in Deployment and CI/CD
- Introduction to AI tools for DevOps (e.g., Harness, CircleCI plugins)
- AI enhanced CI/CD monitoring, anomaly detection and predictive maintenance
- Automating deployment workflows
- LAB: Setting up an AI enhanced CI/CD pipeline
- Collaborative Development with AI
- AI-powered tools for team collaboration (e.g., Slack GPT, Google Bard integrations).
- Real-time code collaboration with AI add-ons.
- Managing version control and code reviews with AI insights.
- LAB: Collaborative project development using AI-powered platforms.
- Data Analytics & Log Analysis for Smarter Development
- Utilizing AI for log analysis, performance monitoring, and usage analytics
- Techniques for transforming raw data into actionable insights
- Incorporating these insights into development and debugging practices
- LAB: Using AI to analyze logs and extract trends to inform development improvements
Day 3: Advanced Applications, ethics and security
- Prompting Engineering Techniques for Developers
- Prompt engineering overview
- Techniques for zero-shot, few-shot, and multi-shot prompting
- Context setting and dynamic prompt engineering
- Chain of Thought (CoT)
- LAB: Using generative AI as a design and architecture collaborator
- Leveraging LLMs for Problem-Solving
- Using LLMs like ChatGPT, Gemini and Claude for debugging, code reviews, and generating
- boilerplate code
- Understanding the limitations and potential biases in AI outputs
- Understand the concept of hallucinations and how to avoid them
- LAB: Using LLMs to tackle complex development challenges
- Building Responsible and Ethical AI Practices
- Importance of fairness, transparency, and accountability in AI-generated code
- Ethical considerations and potential biases when using AI in development
- Guidelines for evaluating AI tools’ outputs responsibly
- Building trust in AI tools: Transparency, explainability, and human oversight
- Regulatory guidelines and best practices for responsible AI adoption
- Ethical considerations in team settings
- LAB: Evaluating AI generated content
- Secure AI-Assisted Development Practices
- Understanding vulnerabilities that may be introduced by AI-generated code
- Explore AI strategies to ensure security and compliance
- Review regulatory and best practice security guidelines for AI generated assets
- LAB: Identifying and mitigating security flaws in applications
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.