What You'll Learn
Prompt Engineering is a cutting-edge course designed for those who wish to master the art of interacting with and manipulating large language models. Through a series of workshops, participants will learn to create complex prompts, develop benchmarks, jailbreak models, automate conversations, and much more. The course emphasizes practical skills alongside theoretical knowledge, preparing students for advanced applications in AI.
Course Schedule
Week 1 2 sessions
Complex Prompts
Build powerful, flexible prompt structures
Complex Prompts
Build powerful, flexible prompt structures
Week 2 2 sessions
Benchmarks & Meta-languages
Evaluate prompts with symbolic reasoning
Benchmarks & Meta-languages
Evaluate prompts with symbolic reasoning
Week 3 2 sessions
Model Context & Tools
Custom tools and model integration
Model Context & Tools
Custom tools and model integration
Week 4 2 sessions
Scheduling & File Access
Automate triggers and file operations
Scheduling & File Access
Automate triggers and file operations
Week 6 2 sessions
Simple RAG
Basic retrieval-enhanced agent design
Simple RAG
Basic retrieval-enhanced agent design
Week 7 2 sessions
Advanced RAG
Sophisticated retrieval with evaluation layers
Advanced RAG
Sophisticated retrieval with evaluation layers
Week 8 2 sessions
Jailbreaking
Break alignment with red-teaming
Jailbreaking
Break alignment with red-teaming
Week 10 2 sessions
Alignment
Add guardrails and judgment systems
Alignment
Add guardrails and judgment systems
All times shown in Pacific Time
Where This Class Leads
What you leave able to do
- govern a swarm by information hierarchy
- reconcile a working set with the context window it must fit
- synthesise a long work that stays consistent across many generations
- synthesise a memory hierarchy for an agent
- synthesise a metalanguage for a problem domain
- synthesise a self extending agent behind a review gate
- synthesise an agent that carries notes across its own runs
How you show it. Paired runs of the same multi-step task with the note store kept and cleared, where the cleared run repeats a step the noted run skips, plus the note text the agent wrote and the later turn that cites it.
It sits on one path
🤖 Build production AI systems from prompt to deployment. 7 of 12 after Intro to Agents · before RAG & MemoryAbout 101 hours of practice sit behind this one. See what your hours buy.
Who Is This Course For
Professionals and enthusiasts with a foundational understanding of large language models looking to expand their skill set in prompt engineering and AI interaction.
Who this is for & what you'll need
Join the Course
Pay what you can - accessible learning for all
We use a suggested price to prevent abuse, but if you need a larger discount, apply for a scholarship. We offer 100% scholarships — no one is turned away for lack of funds.
Suggested price • Pay what you can
- Industry mentors
- Participate in active research projects
- Networking events
- Integrate AI into your workflow from the beginning
- Please self-select into this tier- if you need it, use it
Secure payment via Stripe
Suggested price • Pay what you can
- Industry mentors
- Participate in active research projects
- Networking events
- Integrate AI into your workflow from the beginning
- Help pay for underrepresented folks in tech!
Secure payment via Stripe
Frequently Asked Questions
How are classes delivered?
All classes are live and online via video call. Sessions are recorded so you can review them later if you miss one.
What does "pay what you can" mean?
We show a suggested price, but you can pay any amount. If you need a larger discount, apply for a scholarship — we offer 100% scholarships and no one is turned away for lack of funds.
Do I need prior experience?
🔵 Solo coding. You should be able to write in some language on your own.
What do I get when I enroll?
Live sessions with the instructor, access to course curriculum materials, community access via our Matrix chat server, and recordings of all sessions.
Want to schedule this course for your team?
Contact us: [email protected]