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Dr. Jules White

Unlock the full potential of generative AI and become a master of prompt engineering. Dive deeper into how you can use In-context Learning to build better and more reliable prompts. See how Retrieval Augmented Generation (RAG) works and what can go wrong that you can counteract with fact-checkable prompt formats. Overcome your struggles in getting the right output from generative AI models with template-based output formats to achieve precision in your interactions with AI models. Tap into powerful AI capabilities for tasks ranging from social media comment analysis to survey results interpretation and beyond. This course will empower you with the skills needed to build exceptional prompts, using simple techniques, such as preference-driven refinement, and become and expert in leveraging generative AI for productivity and creativity.

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Unlock the full potential of generative AI and become a master of prompt engineering. Dive deeper into how you can use In-context Learning to build better and more reliable prompts. See how Retrieval Augmented Generation (RAG) works and what can go wrong that you can counteract with fact-checkable prompt formats. Overcome your struggles in getting the right output from generative AI models with template-based output formats to achieve precision in your interactions with AI models. Tap into powerful AI capabilities for tasks ranging from social media comment analysis to survey results interpretation and beyond. This course will empower you with the skills needed to build exceptional prompts, using simple techniques, such as preference-driven refinement, and become and expert in leveraging generative AI for productivity and creativity.

What You Will Learn:

In-Context Learning: Understand how to provide context within prompts to guide AI models towards more accurate and relevant outputs. Learn techniques to embed contextual information that enhances the model’s understanding and performance.

Retrieval Augmented Generation (RAG): Explore how to integrate retrieval systems with generative models to provide more precise and informed responses. This module covers the mechanisms to combine the strengths of both retrieval and generation.

Template Pattern and Examples: Master the art of crafting template-based prompts to achieve consistent and desired outputs. Learn how to tell generative AI to fit its output into your format and overcome struggles with getting exactly what you want.

Tapping Into AI Capabilities for Everyday Use: Delve into practical applications such as analyzing social media comments, interpreting survey results, and other everyday tasks where generative AI can make a significant impact. Gain hands-on experience with real-world examples.

Building Great Prompts: Discover simple methods for constructing effective prompts. Learn how preference-driven refinement can elevate your prompt engineering skills, ensuring that outputs meet your exact needs and preferences.

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What's inside

Syllabus

Writing & In-context Learning
Think More, Not Less, Prompting for Options
Prompt Engineering: Machine Learning for Everyone
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops key skills for building and refining prompts to optimize generative AI output
Taught by Dr. Jules White, a recognized expert in generative AI
Imparts understanding of In-Context Learning, empowering learners to guide AI models toward accurate outputs
Explores Retrieval Augmented Generation (RAG), enabling learners to combine retrieval systems with generative models for precise responses
Offers real-world applications of generative AI in social media analysis, survey interpretation, and other practical contexts

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Reviews summary

Practical prompt engineering for everyone

According to learners, this course provides a clear and practical approach to advanced prompt engineering, making complex concepts like In-Context Learning and Retrieval Augmented Generation (RAG) highly accessible. Students particularly praise the module on template-based output formats as a game-changer for achieving precise AI interactions, leading to enhanced productivity and creativity in daily tasks. While many find the course beneficial for applying AI in their work, some more experienced individuals felt the "advanced" label might be misleading, seeking deeper technical dives or more complex coding examples. Overall, it's considered a solid foundation for those looking to leverage generative AI effectively.
Well-structured content with clear explanations.
"The instructor explains concepts very well. This course is great for productivity."
"The course structure is logical, and the pacing is good. The emphasis on 'simple techniques' is evident, making it accessible."
"This course opened my eyes to the nuances of prompt engineering. The instructor's explanations are superb, and the modules are well-structured."
Master precise AI output with templates.
"I especially appreciated the template-based output formatting module; it's a game-changer for consistent AI interactions."
"Absolutely brilliant! As someone who struggles to get precise outputs from generative AI, the template patterns module was exactly what I needed."
"The section on Retrieval Augmented Generation (RAG) was a highlight for me. It broke down a complex topic into understandable parts."
Learn actionable techniques for everyday AI use.
"The explanations for In-Context Learning and RAG were incredibly clear, and the practical examples truly helped solidify my understanding."
"The hands-on examples and practical exercises were very helpful. I learned how to structure prompts for complex tasks efficiently."
"I learned how to use practical tools and strategies that I could apply immediately to my work."
Better for practical users than deep experts.
"I came in hoping for more 'advanced' techniques. While it covers important concepts, it felt a bit high-level at times, especially if you already have some background."
"Honestly, I found this course a bit basic for something titled 'Advanced.' While it introduces concepts, it doesn't dive deep enough into the underlying mechanics or optimization for complex use cases."
"If you're a beginner to prompt engineering, it's a good starting point. If you're already familiar with the basics, you might find some parts too simple."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Advanced Prompt Engineering for Everyone with these activities:
Review the basics of natural language processing
Refreshing your knowledge of natural language processing will help you better understand the concepts discussed in the course and improve your ability to use generative AI models.
Show steps
  • Read articles and blog posts about natural language processing.
  • Watch videos and tutorials on natural language processing.
  • Complete practice exercises on natural language processing.
Watch tutorials on prompt engineering from PromptBase
PromptBase is a website that provides free tutorials and resources on prompt engineering. Watching these tutorials will help you learn the basics of prompt engineering and get started using generative AI models.
Browse courses on Prompt Engineering
Show steps
  • Visit the PromptBase website.
  • Watch the 'Introduction to Prompt Engineering' tutorial.
  • Watch the 'How to Write Effective Prompts' tutorial.
Review the basics of generative AI
Recall concepts like neural networks, deep learning, and language models to provide a solid foundation for understanding advanced generative AI techniques.
Browse courses on Generative AI
Show steps
  • Revisit materials from previous courses or online resources on generative AI
  • Complete practice exercises to reinforce your understanding of the fundamentals
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio and Aaron Courville
This book provides a comprehensive overview of deep learning, covering its theoretical foundations and practical applications. Reading it will give you a strong foundation in the field and help you better understand the concepts discussed in the course.
View Deep Learning on Amazon
Show steps
  • Read the introductory chapters to get an overview of deep learning.
  • Work through the exercises in the book to practice applying deep learning techniques.
  • Attend the course lectures and discussion forums to supplement your understanding of the material in the book.
Attend a meetup or conference on generative AI
Attending a meetup or conference on generative AI will allow you to connect with other professionals in the field and learn about the latest trends and developments.
Browse courses on Generative AI
Show steps
  • Find a meetup or conference on generative AI to attend.
  • Register for the event.
  • Attend the event and participate in the discussions.
  • Follow up with any new contacts you make.
Practice prompt engineering with Hugging Face's Coursere
Coursere is a free online platform that provides interactive exercises and tutorials on prompt engineering. Practicing with Coursere will help you develop your skills in crafting effective prompts and improve your ability to use generative AI models.
Browse courses on Prompt Engineering
Show steps
  • Create an account on the Coursere website.
  • Enroll in the 'Prompt Engineering' course.
  • Complete the exercises and tutorials in the course.
Explore in-context learning techniques
Learn how to provide context within prompts to guide AI models towards more accurate and relevant outputs.
Show steps
  • Follow online tutorials and workshops on in-context learning
  • Practice using in-context learning techniques in your own projects
Review your notes from the course
Reviewing your notes will help you solidify your understanding of the course material and identify any areas where you need additional support.
Show steps
  • Set aside some time to review your notes.
  • Read through your notes carefully and make sure you understand everything.
  • Identify any areas where you need additional support.
  • Seek out additional resources to help you understand these areas.
Build a project using retrieval augmented generation (RAG)
Gain hands-on experience in integrating retrieval systems with generative models to enhance the precision and informativeness of responses.
Show steps
  • Identify a problem or task that can be solved using RAG
  • Design and implement a RAG system
  • Evaluate the performance of your RAG system
Attend a workshop on leveraging generative AI for productivity and creativity
Gain practical insights and hands-on experience in applying generative AI tools and techniques to enhance your productivity and creativity.
Show steps
  • Identify and attend a relevant workshop or conference
  • Participate actively in the workshop and engage with experts
Write a blog post about your experience using generative AI
Writing a blog post about your experience using generative AI will help you reflect on what you've learned and share your knowledge with others. It will also help you improve your writing skills.
Browse courses on Generative AI
Show steps
  • Choose a topic for your blog post.
  • Write an outline for your blog post.
  • Write the first draft of your blog post.
  • Edit and revise your blog post.
  • Publish your blog post.
Participate in a prompt engineering competition
Participating in a prompt engineering competition will challenge you to develop your skills and test your knowledge. It will also give you the opportunity to win prizes and recognition for your work.
Browse courses on Prompt Engineering
Show steps
  • Find a prompt engineering competition to participate in.
  • Develop your prompt engineering skills.
  • Submit your entry to the competition.
  • Wait for the results of the competition.
  • Reflect on your experience and learn from your mistakes.

Career center

Learners who complete Advanced Prompt Engineering for Everyone will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 42 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Advanced Prompt Engineering for Everyone.
This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.
This practical book dives into using transformers, the dominant architecture for many state-of-the-art NLP tasks, with the Hugging Face Transformers library. , It's ideal for data scientists and coders who want to learn how to build and scale models for various NLP applications using this popular library. It covers contemporary topics and is highly relevant for current NLP practice.
Provides a comprehensive guide to prompt engineering, covering techniques for crafting effective inputs to generative AI models. It's particularly useful for understanding how to obtain reliable and predictable results, which is crucial for both beginners and those looking to deepen their practical skills. This book is valuable as a current reference for anyone working with generative AI.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
This foundational text offers a comprehensive introduction to statistical methods in NLP. It covers the necessary theory and algorithms for building NLP tools, with a rigorous treatment of mathematical and linguistic foundations. While published in 1999, it remains highly relevant for understanding the statistical underpinnings of many NLP techniques and valuable reference for researchers and students alike.
Offers a practical, hands-on approach to prompt engineering specifically with ChatGPT. It's an excellent resource for high school and undergraduate students getting started, providing clear examples and exercises. It serves as a useful introductory guide and additional reading to complement foundational AI courses.
Serves as a practical introduction to NLP with a strong focus on using the Natural Language Toolkit (NLTK) library in Python. It's excellent for beginners and those who want a hands-on approach to learning NLP concepts through programming examples. It's widely used as a textbook and provides a solid foundation for implementing basic NLP tasks.
Provides a hands-on approach to prompt engineering with a focus on using the OpenAI API. It's particularly relevant for undergraduate students, graduate students, and working professionals who want to programmatically interact with LLMs. It serves as a practical guide for implementing prompt engineering techniques in applications.
This textbook offers a technical perspective on NLP, synthesizing classical methods with contemporary machine learning techniques. , It covers topics from basic textual analysis to structured representations and semantic analysis, making it suitable for advanced undergraduate and graduate courses. It requires a background in programming and college-level mathematics.
Focuses on the creative aspects of prompt engineering and generating diverse language outputs. It's a good fit for students and professionals looking to go beyond basic prompting and explore more advanced techniques for creative content generation. It adds breadth by covering applications in areas like creative writing and podcasting.
A comprehensive guide aimed at making prompt engineering accessible to a wide audience. It covers various techniques and applications for leveraging AI language models effectively. is useful for high school students through working professionals seeking a thorough introduction and practical guidance.
This guide aims to make prompt engineering accessible with a step-by-step approach. It is well-suited for beginners and those new to the field, including high school students and those in introductory undergraduate programs. It provides practical tips and is useful for gaining a broad understanding of how to formulate effective AI prompts.
This guide offers quick tips and tricks for prompt engineering across different generative AI models, including text and image generation models. It's a practical resource for users who want to improve their immediate results with various AI tools. It's suitable for all levels looking for actionable advice.
Takes a practical, code-first approach to deep learning, including applications in NLP, using the fastai library and PyTorch. , It's designed for programmers who want to achieve impressive results in deep learning with less emphasis on the underlying mathematics. It's a great resource for quickly getting hands-on experience with modern deep learning techniques in NLP.
Focuses on implementing NLP models using the PyTorch deep learning library. It's a practical guide for those who prefer PyTorch and want to learn how to build various NLP applications using this framework. It's suitable for developers and researchers.
This widely-used textbook provides a comprehensive overview of AI, including chapters on machine learning, natural language processing, and knowledge representation, which are relevant for understanding the context of RAG.
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This practical book focuses on applying text analysis techniques using Python and machine learning. , It's geared towards data scientists and developers who want to build real-world NLP applications. It covers various aspects of the NLP pipeline and is valuable for its applied approach.
Provides a business-oriented perspective on generative AI, discussing its potential impact on industries and how companies can use it to gain a competitive advantage. It is written by three leading experts in the field, Thomas Davenport, Rajeev Ronanki, and Nitin Mittal.
Explores prompt engineering within the broader context of generative AI and touches upon ethical considerations. It's relevant for all levels, providing a balanced view of the technical aspects and the societal impact of generative AI. It's useful for gaining a broader understanding and considering the responsible use of AI.
Provides a broad overview of deep learning techniques for NLP, including chapters on text classification, question answering, and text generation, which can be helpful for understanding the foundations of RAG.
Takes a practical, project-based approach to learning NLP, guiding readers through building various NLP applications using Python libraries. It's focused on applying NLP concepts to solve real-world problems and is suitable for developers and practitioners who want to gain hands-on experience.
Provides a thought-provoking exploration of the future of generative AI, discussing its potential benefits and risks. It is written by Gary Marcus, a leading researcher in the field.
While not solely focused on prompt engineering, this book provides a strong foundation in understanding how LLMs work, which is essential for effective prompting. It's suitable for undergraduate and graduate students, offering technical insights into language understanding and generation. It serves as valuable background reading for those wanting to understand the underlying mechanisms of the models they are prompting. Expected publication in September 2024.
This handbook is geared towards engineers working with LLMs, covering the process from concept to production. It provides a deeper technical understanding of integrating and managing LLMs, which is relevant for prompt engineers working in a development context. It is best suited for graduate students and working professionals.
This comprehensive handbook provides an overview of concepts, methodologies, and applications in computational linguistics and NLP. , It covers a wide range of topics and can serve as a valuable reference for researchers and advanced students looking for detailed information on specific areas within the field.
Covers the broader field of AI engineering with a focus on foundation models, which include the LLMs used in prompt engineering. It's relevant for professionals and graduate students interested in the engineering aspects of building AI applications. It provides context on how prompt engineering fits into larger AI systems.
Focuses on the practical aspects of building NLP systems. While prompt engineering specific technique, understanding the entire NLP pipeline is beneficial for professionals. It's a good reference for those looking to implement prompt engineering within larger NLP applications.
This comprehensive textbook covers the theoretical foundations of pattern recognition and machine learning. While not specific to NLP, the concepts and techniques discussed are widely applicable to NLP tasks. It's a rigorous text suitable for graduate students and researchers seeking a deep understanding of the statistical and mathematical basis of machine learning in NLP.
Is specifically aimed at educators but offers valuable insights into crafting prompts for educational purposes. It's relevant for anyone interested in using prompt engineering in a learning context. It provides practical strategies and examples for a specific application domain.
Explores the relationship between generative AI and the creative process, discussing how generative AI can be used to enhance creativity. It is written by Margaret Boden, a leading researcher in the field.
For those who want to understand the mechanics of LLMs deeply, this book guides you through building one from scratch. This is highly technical and suitable for advanced undergraduate students, graduate students, and researchers. A deep understanding of LLM architecture is beneficial for advanced prompt engineering techniques.
This classic textbook provides a comprehensive introduction to the field of machine learning. , While published in 1997, it covers fundamental machine learning algorithms and concepts that are still highly relevant to NLP. It's a valuable resource for gaining a solid understanding of the machine learning principles applied in NLP. ,
Dedicated resource for the field of statistical machine translation, a significant application area of NLP. It covers the fundamental concepts and techniques used in building machine translation systems. While neural machine translation has become dominant, this book provides essential background on statistical methods.
A beginner-friendly guide to using ChatGPT. is ideal for high school students and those new to generative AI who want a straightforward introduction to interacting with models. It focuses on practical usage and good starting point before diving into more complex prompt engineering concepts.
Explores the potential applications of generative AI in healthcare, discussing how it could be used to improve patient care and accelerate drug discovery. It is written by Eric Topol, a leading researcher in the field.
Explores the philosophical implications of generative AI, discussing how it challenges our understanding of mind and consciousness. It is written by Daniel C. Dennett, a leading philosopher in the field.
Explores the potential applications of generative AI in climate change, discussing how it could be used to model climate change and develop solutions. It is written by Andrew Ng, a leading researcher in the field.
Explores the potential impact of generative AI on the law, discussing how it could be used to automate legal processes and improve access to justice. It is written by Ryan Abbott, a leading researcher in the field.
Explores the potential impact of generative AI on the economy, discussing how it could be used to create new jobs and improve productivity. It is written by two leading experts in the field, Erik Brynjolfsson and Andrew McAfee.
Explores the potential impact of generative AI on society, discussing how it could be used to solve social problems and improve quality of life. It is written by Kai-Fu Lee, a leading researcher in the field.

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