Join our session on “Prompt Engineering Essentials – How to Get the Best from AI” to learn how the quality of prompts directly impacts the outputs generated by AI models. Discover how to structure clear, effective prompts to guide AI systems toward accurate, relevant, and actionable responses. This session will cover practical prompt frameworks, common pitfalls, and real-world examples to help you interact more effectively with AI tools. Whether you’re new to AI or looking to refine your approach, this session will equip you with essential techniques to get the most value from AI.
Agenda for the session
- Learn to communicate effectively with AI and avoid common prompt errors.
- Apply techniques to get accurate and impactful results from AI tools.
- No Code and Agentic AI program
- Live Q&A
About Speakers
Mr. Matthew Nickens
Senior Manager, Data Science at CarMax
Matthew Nickens is a seasoned data-science leader currently serving as Senior Manager of Data Science at CarMax. With prior roles at Meta, The Walt Disney Studios, and 20th Century Fox, he brings broad experience in applying AI and analytics to solve complex problems across industries. He combines deep technical knowledge with a keen sense for practical, business-relevant AI applications, making him ideal to guide learners in mastering prompt engineering for generative AI tools.
No Code and Agentic AI program by MIT Professional Education
No-Code AI is transforming how professionals build and deploy intelligent solutions by enabling them to design AI-driven systems without writing code. As organizations move from AI experimentation to real business impact, the ability to apply Generative and Agentic AI using no-code tools has become a critical skill across industries. To help you stay ahead, MIT Professional Education offers the No Code and Agentic AI program.
In this 14-week program, you will develop a practical understanding of the evolving AI landscape, from machine learning to Generative AI and autonomous agentic systems. You will learn to work with large language models, build AI workflows, and design intelligent agents and multi-agent systems, while applying concepts like regression, decision systems, and recommendation engines. Through a hands-on, no-code approach, you will build real-world AI workflows and applications without writing a single line of code.
