What you will learn

  • Learn how to apply effective prompting techniques with best practices

  • Develop a systematic framework for prompting and building with LLMs

  • Learn from common use cases how to best apply prompt engineering techniques

Course curriculum

    1. Introduction to Prompt Engineering

    2. About the Instructor

    3. Course Objectives

    4. Course Structure

    5. Course Tips

    6. The tools and environment

    7. Setting up your Playground

    8. Setting up the OpenAI Playground

    1. What are LLMs?

    2. Base LLM vs. Instruction-Tuned LLM

    3. LLMs and LLM Providers

    4. Chat LLMs

    5. Chat LLM Common Use Cases

    6. How to Leverage LLMs?

    7. Quiz

    1. What is Prompt Engineering?

    2. Why Prompt Engineering?

    3. Elements of a Prompt

    4. First Basic Prompt

    5. Quiz

    1. Introduction to the OpenAI Playground

    2. OpenAI Playground - Roles

    3. OpenAI Playground - Temperature

    4. OpenAI Playground - Text Classification

    5. OpenAI Playground - Role Playing

    6. Exercise 1: Getting Started with OpenAI Playground

    7. Exercise 2: Text Summarization

    1. What makes a good prompt?

    2. Be clear and specific when prompting

    3. Using delimiters

    4. Specifying output length

    5. Output format

    6. Split Complex Tasks into Subtasks

    1. Introduction to Few-shot prompting

    2. How many demonstrations?

    3. Tips for preparing demonstrations

    4. Quiz

About this course

  • 46 lessons
  • 1.5 hours of video content
  • Projects to apply learnings
  • Earn a Certificate of Completion
  • Beginner

Join Pro to Get Started

Get unlimited access to all our AI courses, special tutorials, certificates, live webinars, workshops, office hours, dedicated support, and community discussions.

Instructor(s)

Elvis Saravia, Ph.D.

Founder and Lead Instructor

Elvis is a co-founder of DAIR.AI, where he leads all AI research, education, and engineering efforts. Elvis holds a Ph.D. in computer science, specializing in NLP and language models. His primary interests are training and evaluating large language models and developing scalable applications with LLMs. He co-created the Galactica LLM at Meta AI and 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 on solutions such as Elasticsearch, Kibana, and Logstash.

OVERVIEW

This course focuses on key prompt engineering techniques for large language models (LLMs) and how to effectively apply them in various scenarios and use cases. After completing this course, students will have a clear and systematic framework for how to effectively and efficiently prompt LLMs to enable a variety of tasks and use cases.

PREREQUISITES

This course doesn't have any prerequisites. The main tool you will use is the OpenAI Playground, therefore, no programming is required. You will need to create a paid account using OpenAI. More details and instructions are provided in the course.

SYLLABUS

Course Introduction

  • Get a complete overview of the course objectives, structure, and tips for success from instructor Elvis Saravia.
  • Learn about the main tool used in this course, the OpenAI Playground, and follow a step-by-step guide to set up your account.


Introduction to LLMs

  • Gain a high-level understanding of what Large Language Models (LLMs) are and the Transformer architecture they are built on.
  • Learn the critical difference between a base LLM and an instruction-tuned LLM, which is designed to follow commands.
  • Discover Chat LLMs like GPT-4o and their unique interface with system, user, and assistant roles that enable complex dialogue.


Introduction to Prompt Engineering

  • Learn prompt engineering as the practice of designing, optimizing, and evaluating prompts to improve LLM performance.
  • Understand the essential elements of a well-formed prompt, including the instruction, context, input data, and output formatting.


The Playground

  • Get a comprehensive tour of the OpenAI Playground and its features for designing, testing, and iterating on prompts.
  • Learn to use key settings like temperature to control response randomness and roles to structure conversations for role-playing or providing examples.
  • Master how to specify a JSON output format to receive structured, machine-readable data from the model.


Improving Prompts

  • Learn the core principles of effective prompting, focusing on how to write clear, specific, and unambiguous instructions.
  • Master techniques like using delimiters (e.g., ### or XML tags) to structure your prompt and specifying output length to control verbosity.
  • Understand the powerful strategy of splitting complex tasks into simpler subtasks to improve model reliability.


Few-shot Prompting

  • Discover the power of few-shot prompting, a technique where you provide the model with examples (demonstrations) to steer its behavior.
  • Learn best practices for selecting and preparing high-quality demonstrations, focusing on relevance, diversity, and format.


Use Case 1: Information Extraction

  • Apply your skills to a practical information extraction task: pulling specific model names from machine learning papers.
  • See firsthand how moving from a zero-shot prompt to a well-crafted few-shot prompt dramatically improves the model's accuracy and reliability.


Chain-of-Thought Prompting

  • Learn to use Chain-of-Thought (CoT) prompting to unlock more advanced reasoning in LLMs.


Use Case 2: Chatbot

  • Combine multiple prompting techniques to build a functional and conversational food chatbot.
  • Implement a detailed, multi-step Chain-of-Thought prompt to help the chatbot reason through user questions about a menu, ensuring factual and relevant responses.


Conclusion

  • Recap the key prompting methods and design patterns covered throughout the course.
  • Look ahead at the future of prompt engineering and explore advanced topics like ReAct, prompt chaining, and systematic evaluation.


TOPICS

Throughout the course, students will utilize the OpenAI Playground to design and optimize their prompts for several use cases.

Key concepts covered in the course include:

  • Introduction to LLMs: Learn the fundamentals of Large Language Models (LLMs), including their core types, applications, and practical implementation strategies. This course covers everything from basic concepts to hands-on usage of chat LLMs, preparing you to effectively leverage LLMs in real-world scenarios.
  • Introduction to Prompt Engineering: Master the skill of designing effective LLM prompts through this foundational course in Prompt Engineering. Learn what makes an effective prompt, why it matters, and how to write your first prompts to get optimal results from LLMs.
  • The OpenAI Playground: Explore OpenAI's Playground interface and learn to control an LLM's behavior through hands-on exercises. Students will learn and apply essential topics, including roles, temperature settings, role-playing, and text classification.
  • Improving Prompts: Elevate your prompt writing skills by learning the key elements of effective prompts. This module covers best practices for clarity, using delimiters, controlling output length, and formatting outputs - essential techniques for getting consistent, high-quality responses from LLMs.
  • Few-shot Prompting: Master the technique of few-shot prompting to improve LLM performance through examples. Learn how to effectively use demonstrations in your prompts, determine the optimal number of examples, and prepare them for the best results.
  • Use Case - Information Extraction: Learn practical applications of prompt engineering for extracting structured information from text. This module covers how to apply both zero-shot and few-shot approaches to help you efficiently pull specific data from various content types.
  • Chain-of-Thought Prompting: Discover how to guide LLMs through complex reasoning using Chain-of-Thought prompting. Practice this powerful technique through hands-on exercises, including a practical case study on movie recommendations, followed by a quiz to test your understanding.
  • Use Case: Chatbot: Apply all the lessons and best practices learned in the course to build and optimize a system prompt for a chatbot application.


Here's a subset of companies whose employees have benefited from our courses:

Accrete, Airbnb, Alston & Bird, Amazon, Apple, Arm, Asana, Bank of America, Belong For Me, Biogen, Brilliant, Carebound, CDM, CentralReach, Centric Software, Chime, Coinbase, Digital Green, DoHQ, Elekta, Fidelity Investments, Fivecast, Fulcrum Labs, Google, Guru, Gretel, Harrison Insights, Intel, Intuit, Jina AI, JPMorgan Chase & Co, Khan Academy, KnowBe4, Lawyer.com, LinkedIn, LionSentry, MagmaLabs, MasterClass, Meta, Metopio, Microsoft, Moneta Health, Oracle, OpenAI, Rechat AI, RingCentral, Salesforce, Scale AI, Scribd, Space-O Technologies, Sun Life, TD Bank, TELUS Corporations, Trilogy, TTEC Digital, UniCredit, VaxCalc Labs, Vendr, Walmart, Wolfram Alpha, Zemoso Technologies, Zeplin

Reach out to [email protected] for any questions and team/student discounts.

Join Pro to Get Started

Get unlimited access to all our AI courses, special tutorials, certificates, live webinars, workshops, office hours, dedicated support, and community discussions.

What people are saying

“ In a rapidly evolving LLM landscape, the live nature of the course enables Elvis to expertly tie in the latest developments when answering questions, provide pointers to foremost resources and share his experience working with many of the tools and frameworks out there. You will not want to miss this course!”

Yevgeniy S. Meyer, Ph.D. | Chief Scientist at Gretel (gretel.ai)

“The course's focus on practical applications, combined with the theoretical underpinnings, makes it a valuable resource for both beginners and experienced data scientists/software engineers.”

Yashwanth (Sai) Reddy | Director, Data Science at Fidelity Investments

“Elvis did a great job of exploring lots of different Prompt Engineering topics, showcased numerous use cases. He also provided us with comprehensive notebooks filled with various examples. The teaching style was really approachable and relaxed, which made for some great live discussions. All in all, it was a pretty solid experience.”

Miguel Won | NLP Data Scientist at Axions Portugal

“I had a fantastic experience taking Elvis’ Prompt Engineering Class. He is incredibly knowledge and has the ability to distill the latest research on prompt engineering to make it accessible to almost anyone.”

Lawrence Wu | Principal Data Scientist at UKG

“This course helped me understand how prompts actually affect model outputs. Before this, I was just trial and erroring. Now I approach prompt writing more like debugging with structure and logic.”

Rahul Jain | Co-Founder & CEO at Pixeldust Technologies

Stay Updated!

Be the first to know about what's new in our Academy.

Thank You