Module 1 · Fundamentals
What We Owe Machines
January 26, 2026The hard problem in AI isn't making machines smarter, it's teaching them to handle problems without right answers. This lecture traces how we learned to teach machines at all, and where that project stands today.
The Lifecycle of a Language Model
February 2, 2026Understanding the full training pipeline from pretraining to deployment.
Module 2 · Post-Training
Post-Training Foundations
February 9, 2026 Final Project A DueRLHF and Reward Learning
February 16, 2026 Project 1 ReleasedAlignment Methods & Model Behavior
February 23, 2026 Final Project B DueEvals as Research
March 2, 2026Module 3 · Reasoning & Agents
Search, Planning, Memory
March 9, 2026 Project 1 DueTool Use and Verification
March 16, 2026 Project 2 ReleasedSpring Break
March 23, 2026No class this week. Enjoy your break!
Multi-Agent Systems
March 30, 2026 Final Project C DueModule 4 · Product & Research
Product Design & Development Workshop
April 6, 2026 Project 2 DueProduct Workshop (Continued)
April 13, 2026Guest Lecture
April 20, 2026Guest Lecture
April 27, 2026Demo Day 🎉
May 4, 2026 · RRR Week Final Project DueFinal Project
Design and build a product demo AI product prototype that demonstrates mastery of course concepts. Teams of 2-4 students will propose, build, and present a demo.
The final project is your opportunity to explore a topic of your choice in depth. Projects should demonstrate technical sophistication and original thinking. Successful projects might included novel alignment techniques, agent architectures, evaluation frameworks, and creative applications of post-training methods.
Milestones
About the Course
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
Grade Breakdown
Assignments
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 (T1–T10) released alongside lectures. These are hands-on labs that reinforce lecture topics and prepare you for the graded projects. Ungraded but strongly recommended.
Projects (graded)
Project 1 focuses on post-training pipelines. Project 2 focuses on agentic systems. Both are graded on completion and quality.
The Final Project is a semester-long endeavor where you ideate, design, prototype, and productionize an AI system. Two checkpoints throughout the semester, culminating in Demo Day on May 4th.
Acknowledgements
- Cognition for providing compute credits
- Thinking Machines for TinkerAPI access