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 Expect

This 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.

• Iteration exercises to explore how small changes can dramatically improve reliability.

• Discussions on user-centric development and integrating feedback loops.

• Expert-led Q&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 will receive

- $1,000 in Modal credits for cloud compute – giving you resources to power real AI applications.

- $1,000 in BaseTen credits for deploying, monitoring, and iterating on LLM-powered apps.

- $300 in Google Cloud and Gemini credits for the first 100 registrants.

- $100 in Mistral Credits for the first 50 registrants.

- $100 in Replicate Credits for the first 50 registrants - ideal for building and deploying multimodal apps. 

- 6 months of HuggingFace Pro for the first 250 registrants - for access to models, datasets, and hosted inference.

- 3-months of access to Learn Prompting Plus (valued at $117): Gain immediate access to over 20 hours of comprehensive courses in Prompt Engineering, Prompt Hacking, AI Image Generation, & more!

- 6 months of free access to Prodigy – a powerful annotation and model improvement tool used for human-in-the-loop training, rapid iteration, and custom NLP workflows. Ines Montani (co-founder of spaCy and Prodigy) will also join us for a guest lecture on human-in-the-loop development and distillation workflows.

- More credits may be announced soon!

Guest lectures

Learn directly from leading practitioners solving real AI problems in production. These sessions focus on practical workflows, debugging strategies, and building scalable systems – not just theoretical research or cutting-edge multi-agent architectures.

Enrolling will also give you access to all recorded guest lectures from Cohort 1 of this course:

- Hamel Husain (Ex-Airbnb, GitHub) – Basic Data Literacy for Debugging and Evaluating LLMs 

- Swyx (Latent Space podcast, smol.ai, ai.engineer): Engineering AI Agents in 2025

- Sander Schulhoff (CEO, LearnPrompting.org) – Prompt Engineering in the LLM SDLC 

- Charles Frye (Developer Advocate, Modal) – Hardware for LLM Developers

- Ravin Kumar (DeepMind) – End-to-End LLM Product Development

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.

• 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.