About

This project-based course teaches you to design, build, post-train, and evaluate agentic AI systems. We connect core technical foundations (model architecture, retrieval-augmented generation, tool use, multi-agent coordination) with hands-on labs and a team capstone project.

You'll gain practical experience with model deployment frameworks, post-training pipelines (SFT, RL, context engineering), evaluation systems, and model-context protocols. The course culminates in building an end-to-end minimum viable AI product.

Emphasis is placed on engineering rigor, creative problem-solving, and deployment at scale.

Learning Outcomes

By the end of this course, you will be able to:

• Design AI systems that are reliable, maintainable, and cost-effective
• Debug and align models in production using modern evaluation and feedback frameworks
• Post-train open-source LLMs using SFT, RL, and context engineering via TinkerAPI
• Translate research into practice, whether founding an AI venture or shipping at scale
• Navigate ethical considerations in deploying autonomous systems
• Prototype and iterate on applications using modern LLM and agentic architectures

Who This Course Is For

Upper-division undergraduates and early graduate students who want to found or join early-stage AI startups, or work on applied ML engineering at major technology companies.

Expected background: Solid Python programming experience (CS 61A/88 + CS 61B or equivalent). Prior ML exposure recommended (CS 182, CS 189, CS 185, or Data 100) but not required. Strong motivation to apply AI research to real-world systems.

Course Structure

• Module 1: Fundamentals Foundations of LLMs, architectures, inference
• Module 2: Post-Training SFT, RLHF, alignment techniques, TinkerAPI
• Module 3: Reasoning and Agents Chain-of-thought, tool use, multi-agent systems
• Module 4: Product and Research Deployment, evaluation, scaling, ethics

Navigating the Course

We keep things simple: one channel for communication, one for submissions, and one site for all course content. Everything you need lives in one of these four places:

• Communication: Slack (see welcome email for invite link)
• Course website: posttraining.ai
• Assignments: Gradescope (enroll with code 6X7K5K)
• Lectures: In-person, recordings available on course site

Attendance (20%)

We believe you learn best by being in class and discussing with your peers. In-person attendance is required with 2 excused absences allowed.

Documentation (10%)

Each week you'll share something you learned: a blog post, tweet, TikTok, LinkedIn post, YouTube video. The format is yours; the goal is building in public.

Labs (ungraded)

Weekly technical work released alongside lectures. These are hands-on labs that reinforce lecture topics. Ungraded but strongly recommended.

Final Project (70%)

The Final Project is a semester-long endeavor where you ideate, design, prototype, and productionize an AI system. Four checkpoints throughout the semester (10% each), culminating in Demo Day.

• Cognition for providing compute credits
• Thinking Machines for TinkerAPI access