The Complete Prompt Engineering for AI Bootcamp (2024)

The Complete Prompt Engineering for AI Bootcamp (2024), available at $119.99, has an average rating of 4.5, with 192 lectures, based on 43520 reviews, and has 111314 subscribers.

You will learn about Learn the strengths and weaknesses of ChatGPT, Midjourney, GitHub Copilot, Stable Diffusion & other major models. Recognize the Five Principles of Prompting, as well as common tips & tricks for professional grade output. Apply what you鈥檝e learned to generate new AI products in 15+ real-world projects for both text and image generation use cases. Understand the Python coding patterns and tooling you need to run and scale AI reliably in production, and start working as an AI Engineer. This course is ideal for individuals who are AI power users who want to learn more advanced practices and learn to run Python code to use AI at scale. or Developers interested in AI and hoping to learn how to get more reliable results in production. or AI Engineers who want to keep up with the latest techniques and developments in the industry. It is particularly useful for AI power users who want to learn more advanced practices and learn to run Python code to use AI at scale. or Developers interested in AI and hoping to learn how to get more reliable results in production. or AI Engineers who want to keep up with the latest techniques and developments in the industry.

Enroll now: The Complete Prompt Engineering for AI Bootcamp (2024)

Summary

Title: The Complete Prompt Engineering for AI Bootcamp (2024)

Price: $119.99

Average Rating: 4.5

Number of Lectures: 192

Number of Published Lectures: 192

Number of Curriculum Items: 192

Number of Published Curriculum Objects: 192

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the strengths and weaknesses of ChatGPT, Midjourney, GitHub Copilot, Stable Diffusion & other major models.

Recognize the Five Principles of Prompting, as well as common tips & tricks for professional grade output.

Apply what you鈥檝e learned to generate new AI products in 15+ real-world projects for both text and image generation use cases.

Understand the Python coding patterns and tooling you need to run and scale AI reliably in production, and start working as an AI Engineer.

Who Should Attend

AI power users who want to learn more advanced practices and learn to run Python code to use AI at scale.

Developers interested in AI and hoping to learn how to get more reliable results in production.

AI Engineers who want to keep up with the latest techniques and developments in the industry.

Target Audiences

AI power users who want to learn more advanced practices and learn to run Python code to use AI at scale.

Developers interested in AI and hoping to learn how to get more reliable results in production.

AI Engineers who want to keep up with the latest techniques and developments in the industry.

Are you eager to dive into the world of AI and master the art of Prompt Engineering? The Complete Prompt Engineering for AI Bootcamp (2024) is your one-stop solution to becoming a Prompt Engineer working with cutting-edge AI tools like GPT-4, Stable Diffusion, and GitHub Copilot!

We update the course every month with fresh content (AI moves fast!):

**Updated July, 2024 – “Five proven prompting techniques and an advanced prompt optimization case study.”   

**Updated June, 2024 – “LangGraph content including human in the loop, and building a chat bot with LangGraph.”   

**Updated: May, 2024 鈥?“ChatGPT desktop, apps with Flask + HTMX, and prompt optimization DSPy, LM Studio”

**Updated: April, 2024 鈥?“LangChain agents, LCEL, Text-to-speech, Summarizing a whole book, Memetics, Evals, DALL-E”

**Updated: March, 2024 鈥?“More content on vision models, and evaluation as well as reworking old lessons.”

**Updated: February, 2024 鈥?“Completely reworked the five principles of prompting + added one pager.”

**Updated: January, 2024 鈥?“Added a one-pager graphic and fixed various errors in notebooks.”

**Updated: December, 2023 鈥?“Another 10 lessons, including creating an entire ebook and more LCEL.”

**Updated: November, 2023 鈥?“10 fresh modules, with 5 covering LangChain Expression Language (LCEL).”

**Updated: October, 2023 鈥?“12 more lessons including GPT-V Vision, Github Co-pilot, LangChain and more.”

**Updated: September, 2023 鈥?“10 more lessons, including projects, more LangChain, non-obvious tactics & SDXL.”

**Updated: August, 2023 鈥?“10 lessons diving deep into LangChain, plus upgraded 9 lessons from GPT-3 to GPT-4.”

**Updated: July, 2023 鈥?“built out the prompt pack, plus 10 more advanced technical lessons added.”

**Updated: June 2023 鈥?“added 6 new lessons and 4 more hands-on projects to apply what you learned.”

**Updated: May, 2023 鈥?“fixed issues with hard to read text mentioned in reviews, and added 15 more videos.”

**Launched: April, 2023

Before we made this course we had both been experimenting with Prompt Engineering since the GPT-3 beta in 2020, and DALL-E beta in 2022, way before ChatGPT exploded on the scene. We slowly replaced every part of our work with AI, and now we work full time in Prompt Engineering. This course is your guide to doing the same and accelerating your career with AI.

*Since launching this course, Mike and James have been commissioned to write a book for O’Reilly titled “Prompt Engineering for Generative AI”*

If you buy this course you get a PDF of the first chapter free! The book is complementary to the course, but with all new material based on the same principles that work.

Whether you’re an aspiring AI Engineer, a developer learning Prompt Engineering, or just a seasoned professional looking to understand what’s possible, this comprehensive bootcamp has got you covered. You’ll learn practical techniques to harness the power of AI for various professional applications, from generating text and images to enhancing software development and boosting your creative projects.

! Warning !: The majority of our lessons require reading and modifying code in Python (for each lesson marked with “- Coding” in the title). Please don’t buy this course if you can’t code and aren’t seriously dedicated to learning technical skills. We’ve heard from non-technical people they still got value from seeing what’s possible, but please don’t complain in the reviews 馃槈

The number of papers published on AI every month is growing exponentially, and it鈥檚 becoming increasingly difficult to keep up. The open-source project Stable Diffusion is the fastest growing repository in GitHub in history, and ChatGPT is the fastest growing consumer product in history, hitting 1 million users in less than a week and 100m in a few months.

This course will walk you through:

Introduction to Prompt Engineering and its importance

Working with AI tools such as ChatGPT, GPT-4, Midjourney, GitHub Copilot, GPT-4, DALL-E, and Stable Diffusion

Understanding the capabilities, limitations, and best practices for each AI tool

Mastering tokens, log probabilities, and AI hallucinations

Generating and refining lists, summaries, and role prompting

Utilizing AI for sentiment analysis, contextualization, and step-by-step reasoning

Techniques for overcoming token limits and meta-prompting

Advanced AI applications, including inpainting, outpainting, and progressive extraction

Leveraging AI for real world projects like generating SEO blog articles and stock photos

Advanced tooling for AI engineering like Langchain and AUTOMATIC1111

We’ve had over 3,000 5-Star Reviews!

Here’s what some students have to say:

“Practical, fast and yet profound. Super bootcamp.” 鈥?Barbara Herbst

“This is a very good introduction about how AI can be prompt-engineered. The instructor knows what he’s talking about and presents it very clearly.” 鈥?Eve Sapsford

“Awesome course for beginners and coders alike! Thoroughly enjoyed myself and the guys delivered some great insights, explaining everything in a straight forward way. Would highly recommend to anyone” 鈥?Jeremy Griffiths

“This is a very good introduction about how AI can be prompt-engineered. The instructor knows what he’s talking about and presents it very clearly.” 鈥?Hina Josef Teahuahu

“The course is quite detailed, I think almost every topic is covered. I liked the coding parts especially.” 鈥?Gyanesh Sharma

“Loved how your articulated the value of thoughtfully engineered prompts. The hands-on exercises were insightful.” 鈥?Akshay Chouksey

“Good content but at few steps voice sounds very robotic, which is funny considering the course is about AI.” 鈥?Shrish Shrivastava

“Awesome and Detailed Course. Helped a lot to understand the nuances of prompt engineering in AI.” 鈥?Prasanna Venkatesa Krishnan

鈥淭he best parts of the online training were demonstrations and real-life hints. Interesting and useful examples鈥?/p>

“Good” 鈥?Jayesh Khandekar

“Mike and James are very good educators and practitioners. Mike also has courses on LinkedIn; together with James, they are running Vexpower. The price is low to collect reviews. It will go up, for sure. GET” 鈥?Periklis Papanikolaou

“This course is a legit practical course for prompt engineering, I learned a lot from this course. The resources that they provided is good, but some of the course (tagged with ‘Coding’ in the Course Title) is for intermediate or advance people in Python programming. If you are not usual with Python, this will be a challenge (like me), but we can overcome it because they taught us step by step pretty clearly (of course I need to pause or backwards). Thanks for this course, but you guys can provide more real case scenario when using AI (less/without coding maybe)” 鈥?J Arnold Parlindungan Gultom

So why wait? Boost your career and explore the limitless potential of AI by enrolling in The Complete Prompt Engineering for AI Bootcamp (2023) today!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the course

Lecture 2: What is Prompt Engineering?

Lecture 3: Accessing resources and prompts

Lecture 4: Optional videos to only do if you know coding

Lecture 5: ChatGPT AI Prompt Pack – 690 Effective Prompts

Lecture 6: Using OpenAI Playground

Chapter 2: Five Principles of Prompting

Lecture 1: Give Direction

Lecture 2: Specify Format

Lecture 3: Provide Examples

Lecture 4: Evaluate Quality

Lecture 5: Divide Labor

Lecture 6: Applying The Five Principles + Worksheet & One Pagers

Chapter 3: How does AI work?

Lecture 1: What are Tokens?

Lecture 2: Log Probabilities

Lecture 3: AI Hallucinations

Chapter 4: Standard Text Model Practices

Lecture 1: List Generation

Lecture 2: Sentiment Analysis

Lecture 3: Explain It Like Im Five

Lecture 4: Least to Most

Lecture 5: Writing Clear Instructions – Detailed Instructions

Lecture 6: Writing Clear Instructions – Specifying the Steps

Lecture 7: Writing Clear Instructions – Delimiters

Lecture 8: Writing Clear Instructions – Specifying Length

Lecture 9: Lets Think Step by Step

Lecture 10: Role Prompting

Lecture 11: Ask for Context

Lecture 12: Question Rewriting

Lecture 13: Pre-Warming Chats

Lecture 14: Progressive Summarization

Lecture 15: Overcoming the Token Limit in ChatGPT

Lecture 16: Tell me a funny joke

Chapter 5: Advanced Text Model Techniques

Lecture 1: Meta Prompting

Lecture 2: Chain of Thought Reasoning

Lecture 3: Prompt Injection

Lecture 4: Automatic Prompt Engineer

Lecture 5: Github Repository for the Course

Lecture 6: Advanced List Generation – Coding

Lecture 7: Prompt Optimization – Coding

Lecture 8: Overcoming Token Limit – ChatGPT – Managing the Message History – Coding

Lecture 9: Vector Databases – Coding

Lecture 10: Reason and Act (ReAct) – Coding

Lecture 11: Recursive Re-prompting and Revision – Coding

Lecture 12: Information Retrieval with Vector Databases – Coding

Lecture 13: AI Resource Hub

Chapter 6: Deep Dive on LangChain – Coding

Lecture 1: What Is LangChain? – Coding

Lecture 2: Installation – Coding

Lecture 3: Chat Models – Coding

Lecture 4: Chat Prompt Templates – Coding

Lecture 5: Streaming – Coding

Lecture 6: Output Parsers – Coding

Lecture 7: Summarizing Large Amounts of Text – Coding

Lecture 8: Document Loaders, Text Splitting & Creating LangChain Documents – Coding

Lecture 9: Tagging Documents – Coding

Lecture 10: Tracing with LangSmith – Coding

Lecture 11: LangChain Hub – LangSmith – Coding

Lecture 12: LCEL – The Runnable Protocol – Coding

Lecture 13: LCEL – Chat Models, itemgetter & RAG – Coding

Lecture 14: LCEL – Chat Message History & Memory – Coding

Lecture 15: LCEL – Creating Multiple Chains – Coding

Lecture 16: LCEL – Conditional Logic, Branching & Merging – Coding

Lecture 17: Using JSON Mode with LangChain – Coding

Lecture 18: Exercise – Using JSON Mode with LangChain – Coding

Lecture 19: LCEL – with JSON Mode – Coding

Lecture 20: LCEL – with OpenAI Functions & JSON mode – Coding

Lecture 21: Exercise – LCEL – with OpenAI Functions & JSON mode – Coding

Lecture 22: LangChain Vector Databases + the Indexing API – Coding

Lecture 23: LCEL Configurable Fields – Coding

Lecture 24: LangChain Agents & Tools – Coding

Chapter 7: Deep Dive On LangGraph – Coding

Lecture 1: Introduction To LangGraph – Coding

Lecture 2: Simple LangGraph Flows – Coding

Lecture 3: Tool Usage and Persistence – Coding

Lecture 4: Human In The Loop – Coding

Lecture 5: Manually Updating The State – Coding

Lecture 6: Customizing State in LangGraph – Coding

Lecture 7: Time Travel – Coding

Lecture 8: RAG in LangGraph (Self Corrective RAG)

Lecture 9: Extra Content To Explore In Your Own Time (Advanced Branching/Subgraphs – Coding

Chapter 8: Proven Prompting Techniques

Lecture 1: Chain of Thought

Lecture 2: Emotion Prompting

Lecture 3: Role Prompting

Lecture 4: In Context Learning

Lecture 5: Self-Consistency Sampling

Chapter 9: Prompt Optimization & Evals

Lecture 1: What are Evals?

Lecture 2: Prompt Testing in GSheets (without code)

Lecture 3: LLM & Image Model Performance: Advanced Evaluation Strategies – Coding

Lecture 4: Eval for a RAG system

Lecture 5: Prompt Optimization with DSPy – Coding

Lecture 6: Eval metrics with DSPy – Coding

Lecture 7: 1: Prompt Optimization: 5 Principles of Prompting – Coding

Lecture 8: 2: Prompt Optimization: Advanced – Coding

Chapter 10: AI Text Model Projects

Instructors

Mike Taylor
Prompt Engineer

James Phoenix
Full Stack Data Engineer

Rating Distribution

1 stars: 242 votes

2 stars: 482 votes

3 stars: 4224 votes

4 stars: 17529 votes

5 stars: 21166 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!