4.5
(80 ratings)
4 Days
·Cohort-based Course
Use advanced prompting techniques and tools to improve the capabilities, performance, and reliability of LLM-powered applications
This course is popular
3 people enrolled last week.
4.5
(80 ratings)
4 Days
·Cohort-based Course
Use advanced prompting techniques and tools to improve the capabilities, performance, and reliability of LLM-powered applications
This course is popular
3 people enrolled last week.
TRUSTED BY
Course overview
OVERVIEW OF THE COURSE
LLMs (Large Language Models) show powerful capabilities, but not knowing how to effectively and efficiently use them often leads to reliability and performance issues. Prompt engineering helps to improve discover capabilities, improve reliability, reduce failure cases, and save on computing costs when building with LLMs.
This is a hands-on, technical course that teaches how to effectively build with LLMs. It covers the latest prompting techniques (e.g., fe-shot, chain-of-thought, RAG, prompt chaining) that you can apply to a variety of complex use cases such as building personalized chatbots, LLM-powered agents, prompt injection detectors, LLM-powered evaluators, and much more.
Topics include:
• Taxonomy of Prompting Techniques
• Tactics to Improve Reliability
• Structuring Inputs and LLM Outputs
• Zero-shot/Few-shot/Many-Shot Prompting
• Chain of Thought Prompting
• Self-Reflection & Self-Consistency
• Meta Prompting & Automatic Prompt Engineering
• ReAcT Prompting Framework
• Retrieval Augmented Generation (RAG)
• Fine-Tuning LLMs
• Function Calling & Tool Usage
• LLM-Powered Agents (Agentic Workflows)
• LLM Evaluation & Judge LLMs
• AI Safety & Moderation Tools
• Adversarial Prompting (Jailbreaking and Prompt Injections)
• Common Real-World Use Cases of LLMs
• Overview of LLM Tools
... and much more
PREREQUISITES
• This is a technical course and you need to be knowledgeable in Python to take this course.
• Basic knowledge of LLMs is beneficial but not required.
If you don't have experience using Python, we recommend our beginner's course: https://maven.com/dair-ai/llms-for-everyone
ABOUT THE INSTRUCTOR
Elvis, the instructor for this course, has vast experience doing research and building with LLMs and Generative AI. He is a co-creator of the Galactica LLM and author of the popular Prompt Engineering Guide. He has worked with world-class AI teams like Papers with Code, PyTorch, FAIR, Meta AI, Elastic, and many other AI startups.
Reach out to training@dair.ai for any questions, corporate trainings, and group/student discounts.
WHO THE COURSE HAS HELPED
This course has helped AI startups freelancers, and professionals at companies like Apple, Microsoft, Google, LinkedIn, Amazon, Coinbase, Asana, Airbnb, Intuit, JPMorgan Chase & Co, and many others.
01
Developers building applications on top of LLMs
02
Builders wanting to improve LLM reliability, efficiency, and performance for their LLM-powered applications.
03
Professionals interested in leveling up on how to better use and apply LLMs.
Design and optimize prompts
Build a robust framework to effectively apply advanced prompt engineering techniques
Develop use cases and build applications
Perform evaluations for your applications
Learn prompt engineering tools
Interactive live sessions
Lifetime access to course materials
In-depth lessons
Direct access to instructor
Projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Advanced Prompt Engineering for LLMs
4.5
(80 ratings)
Elvis is a co-founder of DAIR.AI, where he leads all AI research, education, and engineering efforts. His primary interests are training and evaluating large language models and developing applications on top of them. He is the co-creator of the Galactica LLM and was a technical product marketing manager at Meta AI where he supported and advised world-class teams like FAIR, PyTorch, and Papers with Code. Prior to this, he was an education architect at Elastic where he developed technical curriculum and courses.
Join an upcoming cohort
Cohort 13
$800
Dates
Payment Deadline
Bulk purchases
Yevgeniy S. Meyer, Ph.D.
Miguel Won
Yashwanth (Sai) Reddy
Lawrence Wu
4 Day Intensive
Live Sessions
October 21-24 (8:00 - 10:30 AM PST)
4 live sessions (includes lectures, demos, exercises, and projects)
Live Office Hours
1 hour
Optional office hours to ask questions and receive guidance related to the course topics
1-on-1 Sessions
30 mins
Book a free 1-on-1 session with the instructor to further discuss careers, products, use cases, or anything related to building with LLMs.
Bonus content
2 hours per week
Includes additional readings and self-paced tutorials + bonus exercises to practice prompt engineering techniques and tools for different use cases and applications
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
What happens if I can’t make a live session?
What are the prerequisites for this course?
What tools will we use for the course?
Join an upcoming cohort
Cohort 13
$800
Dates
Payment Deadline
Bulk purchases