Teaching
Together with Aishwarya Naresh Reganti, I create learning resources and teach courses on building AI systems. We cover how to choose a useful problem, build the application, and improve it once people are using it.
Free resources
The AI Builder’s Handbook
A guide to LLM fundamentals, agents, evaluation, and production AI, with a learning path to help you decide what to study next.
Awesome Generative AI Guide
A collection of learning paths, free courses, research summaries, and notebooks. Use it to study a topic in depth or find examples to work through at your own pace.
Live training
Our Maven courses offer guided projects and time to work through questions together. Each course page lists current dates and available formats, including self-paced options.
Building Agentic AI Applications with a Problem-First Approach
Start here if you want to take an AI application from a use case to a working system. Covers retrieval, tools, memory, and evaluation, with attention to quality, latency, and cost. You’ll apply the material in a capstone project.
Beyond Evals: Designing Improvement Flywheels for AI Products
For teams with an AI application who need to turn evaluation results and user feedback into product improvements. Covers reference datasets, task-specific metrics, LLM judges, and production monitoring, including how to feed what you learn back into testing and development.
Harness Engineering: Designing the Operating Layer for Reliable Agents
Take this when you need more control over how your agents run. Covers context and memory, tool use, task limits, permissions, delegation, and verification. You’ll build an agent harness and adapt an existing one to your use case.