Cohort 1

Build AI-powered software reliably & from first principles. Learn the GenAI software development lifecycle: agents, evals, iteration & more

Hugo Bowne-Anderson

Host of Vanishing Gradients podcast; ex-Outerbounds; expert in DS/ML.

Stefan Krawczyk

Agentforce; Ex-Stitch Fix; 13+ Years building / productionizing data, ML, Agents

Building AI Applications for Data Scientists and Software Engineers

Stitch Fix

LinkedIn

Outerbounds

Stanford University

Yale University

If you’re a software engineer or data scientist, chances are you’ve built or seen a proof of concept (POC) for a generative AI app. It’s exciting at first—an impressive demo that showcases the potential of large language models. But here’s the problem: that’s where it usually stops. These POCs often fail to scale into reliable, production-ready applications. The result? Endless iteration cycles, unreliable outputs, and frustration as teams struggle in what we call “POC purgatory.” It doesn’t have to be this way. With the right principles, processes, and workflows, you can build LLM applications that work in production – without overcomplicating pipelines or chasing trends. This course focuses on the iterative development, evaluation, and debugging practices that make AI systems production-ready. We’ll explore tools like RAG, embeddings, and agents, but the emphasis is on building scalable, robust systems – ensuring you leave with the mindset and skills to adapt as the technology evolves. You’ll not only learn how to design, test, and deploy generative AI applications, but also how to iterate and improve rapidly. This isn’t just about building something cool – it’s about creating AI systems that solve real problems, deliver real value, and scale with confidence. What to ExpectThis is a highly interactive course designed to get you results. Across live workshops, discussions, and hands-on projects, you’ll build your skills while solving real-world challenges. •	Live coding sessions to design and refine LLM applications.•	Hands on exercises to explore how small changes can dramatically improve reliability.•	Discussions on user-centric development and integrating feedback loops.•	Expert-led Q&amp;As to help you tackle your toughest challenges. What You’ll Learn •	How to transform POC demos into production-grade applications.•	Strategies for managing non-determinism and optimizing outputs through prompt engineering.•	How to monitor, log, and debug AI systems to ensure reliability and performance.•	Best practices for handling structured outputs and integrating function calling into your applications.•	Building workflows for iterative development and experimentation.•	Practical skills through hands-on app development, including creating a Generative AI app to query PDFs. Bonus Perks All students receive over $1,250 in credits from top companies. We’re finalizing additional partner credits and will announce them soon. Past cohorts have typically received $1,500+ in total credits; one cohort topped $2,500 when multiple partners stacked. (Actual amounts vary.) You will receive - $500 in Modal credits for cloud compute (confirmed).- $500 of credits from Pydantic for observability (confirmed).- $250 in Chroma Cloud credits for serverless vector databases++.- Additional partner credits: announcing soon.- Past totals: $1,500+ typical; one cohort &gt;$2,500.- Recent partners: Google Cloud &amp; Gemini, Hugging Face Pro, Prodigy, Learn Prompting Plus, Replicate, and Mistral. Guest lectures Learn directly from practitioners shipping AI/LLM systems in production. This cohort's guest speakers will include - Samuel Colvin (Pydantic)- Jeff Huber (Chroma)- Natalia Rodnova (Eon)- Thomas Wiecki (PyMC Labs)- Ravin Kumar (DeepMind) We'll announce more guest speakers soon. Enrollment includes on-demand access to all recorded guest talks from prior cohorts (slides/resources where available). Previous guest speakers in previous cohorts include: - Jason Liu (Instructor, 567 Labs)- Shreya Shankar (UC Berkeley, ex-Google Brain)- Zach Mueller (Hugging Face)- Paige Bailey (Google DeepMind)- Ines Montani (Explosion AI, spaCy)- Alan Nichol (Rasa)- Nathan Danielsen (Carvana)- Charles Frye (Modal)- John Berryman (Arcturus, ex-Github)- Skylar Payne (AI executive for startups. Ex-Google. Ex-LinkedIn)- Vincent Warmerdarm (marimo)- Samuel Colvin (Pydantic, Logfire)- Hamel Husain (Parlance Labs) This Course Is For •	Data scientists and machine learning engineers who are sick of unreliable POCs and want to ship reliable LLM applications.•	Software engineers who want to learn how to build generative AI systems and master the LLM software development lifecycle. This Course Is Not For Those •	Looking for an introductory course in AI—some programming knowledge is required.•	Expecting ready-made solutions for AI problems without active participation.•&nbsp;Expecting deep dives into advanced evals, multi-agent systems, or large-scale RAG pipelines. This course emphasizes building and iterating on practical, production-ready systems.•	Unable to commit to hands-on projects, live discussions, and iterative development exercises. Why Take This Course By the end of this course, you’ll: •	Understand the first principles of generative AI development.•	Gain hands-on experience building robust, scalable applications.•	Learn how to troubleshoot, monitor, and optimize AI systems for production.•	Develop workflows to iterate and refine AI applications effectively.•	Leave with the confidence to deploy reliable AI systems at scale.

Master the patterns and processes that underpin successful GenAI app development

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

Who is this course for

What you’ll get out of this course

You’ve seen impressive generative AI demos—but how do you take them to production? In this course, you’ll learn the exact workflows, tools, and engineering practices needed to turn concepts into reliable, scalable applications that deliver real value.

Move beyond POCs and build production-ready AI systems

Struggling with inconsistent outputs? Through interactive exercises, you’ll explore proven techniques for optimizing prompts and ensuring your AI delivers actionable results in production settings.

Master prompt engineering for reliable results

When your AI application breaks, you’ll know exactly how to fix it. We’ll walk you through real-world debugging sessions, monitoring techniques, and tools to ensure your systems stay reliable at scale.

 Learn to debug and monitor AI systems like a pro

Want your AI to produce precise, usable results? You’ll learn how to handle structured outputs and function calls, making your applications smarter and more user-friendly.

Build smarter applications with structured outputs

Stop wasting time in endless iteration cycles. Through guided projects, you’ll discover how to set up workflows that refine your applications faster and with less frustration.

Accelerate your development process with iterative workflows

Tired of theoretical knowledge? You’ll build a fully functional app that uses text and image models to query PDFs, giving you practical experience solving complex real-world problems.

Develop hands-on expertise by building GenAI and LLM apps

Whether you’re looking to lead AI projects, impress stakeholders, or level up your technical expertise, this course gives you the tools to stand out as an AI/ML practitioner.

Advance your career with practical, proven techniques

These aren’t just skills for demos—they’re skills for delivering scalable, production-ready systems that solve real-world business challenges.

Solve Real Problems at Scale

Leave the course with a project you can add to your portfolio, demonstrate to your boss, or use to showcase your ability to deploy reliable AI systems.

Showcase Tangible Results

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

Chair, Dept of Medicine, Jersey City Medical Center

CEO, Google's first Chief Decision Scientist, AI Adviser, Decision Strategist, Keynote Speaker (makecassietalk.com), LinkedIn Top Voice

What people are saying

Meet your instructor

Mondays and Wednesdays

To ensure hands-on practical time, there will be project work to complete throughout the course.

Weekly projects

Course schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

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