Lightning Lessons

Context Engineering for AI Agents

Hosted by Hugo Bowne-Anderson and John Berryman

Wed, Oct 1, 2025

11:00 PM UTC (30 minutes)

Virtual (Zoom)

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Building LLM Applications for Data Scientists and Software Engineers
Hugo Bowne-Anderson and Stefan Krawczyk
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What you'll learn

Understand context engineering as empathy with the model

Context engineering = empathizing with AI: shape memory + structure context to guide its “mental state” & boost output.

Keep agents effective through participatory workflows

Unguided agents fail. Reliability comes from user empathy: transparent steps + artifacts that keep humans in control.

Design AI applications that balance model and user needs

Learn to build AI apps that balance empathy for the model and the user to ensure power and trustworthiness.

Why this topic matters

Context engineering is the core of building agentic AI. It means shaping how models perceive, remember, and reason—while empathizing with their limits to prevent breakdowns. Equally, it means empathizing with users by designing transparent, participatory workflows. The result: agents that are reliable, trustworthy, and genuinely useful.

You'll learn from

Hugo Bowne-Anderson

Podcaster, Educator, DS & ML expert

Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He is the host of the industry Vanishing Gradients, where he explores cutting-edge developments in data science and artificial intelligence. As a data scientist, educator, evangelist, content marketer, and strategist, Hugo has worked with leading companies in the field. His past roles include Head of Developer Relations at Outerbounds, a company committed to building infrastructure for machine learning applications, and positions at Coiled and DataCamp, where he focused on scaling data science and online education respectively. Hugo's teaching experience spans from institutions like Yale University and Cold Spring Harbor Laboratory to conferences such as SciPy, PyCon, and ODSC. He has also worked with organizations like Data Carpentry to promote data literacy. His impact on data science education is significant, having developed over 30 courses on the DataCamp platform that have reached more than 3 million learners worldwide. Hugo also created and hosted the popular weekly data industry podcast DataFramed for two years. Committed to democratizing data skills and access to data science tools, Hugo advocates for open source software both for individuals and enterprises.

John Berryman

AI Consultant and Builder, Co-Author of Prompt Engineering for LLMs, ex-Github

John Berryman is the founder of Arcturus Labs, where he helps teams build AI applications. He was an early engineer on GitHub Copilot, contributing to both its code completions and chat features, and is the coauthor of Prompt Engineering for LLMs (O’Reilly).


Before Copilot, John worked in search – helping build systems for the US Patent Office, Eventbrite, and GitHub. He also coauthored Relevant Search (Manning), a guide to modern search technology.


With experience spanning foundational search and cutting-edge LLM tools, John brings a deep, practical understanding of how to design smart, useful AI systems.

Previously at

GitHub
Yale University

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