Learn Prompting 101: Prompt Engineering Camp
This summer, Skill Samurai is excited to launch the world's first Prompt Engineering Camp.
Prompt engineering is a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results. Prompting is the equivalent of telling the Genius in the magic lamp what to do.
The use and accessibility of large language models (LLMs) are advancing rapidly, resulting in increased adoption and interaction between humans and artificial intelligence (AI). A recent report from Reuters suggests that OpenAI's ChatGPT has already attracted 100 million monthly users in just two months since its launch, underscoring the importance of understanding how to communicate with models like ChatGPT and how to maximize their potential. This is where prompt engineering comes in. As AI, machine learning, and LLMs become more integrated into everyday tasks, prompt engineering could potentially become a vital skill and even a standalone job title.In this article, we will delve into the concept of prompt engineering, its significance, and challenges. Additionally, we will provide a comprehensive review of the Learn Prompting course, which is an open-source, interactive course that aims to teach learners of all levels how to practically apply prompting (no prior knowledge of machine learning is required!).
Skill Samurai has partnered with Towards AI and numerous generous contributors. Furthermore, Towards AI and Learn Prompting have partnered to launch the HackAPrompt Competition, which is the world's first prompt hacking competition. Participants do not require a technical background and will be challenged to hack several increasingly secure prompts. To learn more about the competition and to stay updated on the dates and prizes, please follow our Learn AI Discord community or the Learn Prompting Discord community.
Prompting is a vital part of communicating with generative AI models, which primarily interact with users through textual input. In broad terms, a prompt is what users ask the model to do. Prompting is the method by which we tell an AI agent what we want and how we want it using human language adapted for machines. A prompt engineer is responsible for translating the user's ideas from regular conversational language into clear and optimized instructions for the AI.
The output generated by AI models depends heavily on the prompt used. The purpose of prompt engineering is to design prompts that elicit the most relevant and desired response from a Large Language Model (LLM). This involves understanding the model's capabilities and crafting prompts that effectively utilize them.
Prompting is essential because it serves as the bridge between humans and AI, enabling us to communicate and generate results that align with specific needs. By providing a specific prompt, we can guide the model to generate output that is most relevant and coherent in context. This increases control and interpretability, reduces potential biases, and improves the safety of the model.
Prompting also allows for experimentation with diverse types of data and different ways of presenting that data to the language model. It enables us to determine what good and bad outcomes should look like by incorporating the goal into the prompt. Furthermore, prompting can guide the model in the right direction by prompting it to cite correct sources and defend against prompt hacking, where users send prompts to produce undesired behaviors from the model.
In the case of image generation models, such as Stable Diffusion, the prompt is primarily a description of the image the user wants to generate. The precision of the prompt directly impacts the quality of the generated image, and a better prompt leads to a better output.
Overall, prompting is a powerful technique in generative AI that can improve the quality and diversity of generated text. By understanding the specific model and crafting effective prompts, we can fully utilize the capabilities of generative AI and generate results that align with our needs
In the following example, you can observe how prompts impact the output and how generative models respond to different prompts. Here, the DALLE model was instructed to create a low-poly style astronaut, rocket, and computer. This was the first prompt for each image:
- Low poly white and blue rocket shooting to the moon in front of a sparse green meadow
- Low poly white and blue computer sitting in a sparse green meadow
- Low poly white and blue astronaut sitting in a sparse green meadow with low poly mountains in the background
This image was generated by the prompt above:
Although the results are decent, the style just wasn’t consistent. Although, after optimizing the prompts to:
- A low poly world, with a white and blue rocket blasting off from a sparse green meadow with low poly mountains in the background. Highly detailed, isometric, 4K.
- A low poly world, with a glowing blue gemstone magically floating in the middle of the screen above a sparse green meadow with low poly mountains in the background. Highly detailed, isometric, 4K.
- A low poly world, with an astronaut in a white suit and blue visor, is sitting in a sparse green meadow with low poly mountains in the background. Highly detailed, isometric, 4K.
This image was generated by the prompt above:
These images are more consistent in style, and the main takeaway is that prompting is very iterative and requires a lot of research. Modifying expectations and ideas is important as you continue to experiment with different prompts and models.
Here is another example (on a text model, specifically it was with ChatGPT) of how prompting can optimize the results and help you generate accurate results.
While prompting enables the efficient utilization of generative AI, its correct usage for optimal output faces various challenges and brings several security challenges to the fore.
Prompting for Large Language Models can present several challenges, such as:
- Achieving the desired results on the first try.
- Finding an appropriate starting point for a prompt.
- Ensuring output has minimal biases.
- Controlling the level of creativity or novelty of the result.
- Understanding and evaluating the reasoning behind the generated responses.
- Wrong interpretation of the intended meaning of the prompt.
- Lack of the right balance between providing enough information in the prompt to guide the model and allowing room for novel or creative responses.
The rise of prompting has led to the discovery of security vulnerabilities, such as:
- Prompt injection, where an attacker can manipulate the prompt to generate malicious or harmful output.
- Leak sensitive information through the generated output.
- Jailbreaking the model, where an attacker could gain unauthorized access to the model’s internal states and parameters.
- Generate fake or misleading information.
- The model’s ability to perpetuate societal biases if not trained on diverse and minimally biased data.
- Generate realistic and convincing text that can be used for malicious or deceitful purposes.
- The model may generate responses that violate laws or regulations.
As technology advances, the ability to communicate effectively with artificial intelligence (AI) systems has become increasingly important. It is possible to automate a wide range of tasks that currently consume large amounts of time and effort with AI. AI can either complete or provide a solid starting point for all tasks, from writing emails and reports to coding. This resource is designed to provide non-technical learners and advanced engineers with the practicable skills necessary to effectively communicate with generative AI systems.
The camp "Learn Prompting" is an interactive open-source program that teaches applied prompt engineering techniques and concepts. It caters to both beginners and experienced professionals who are interested in expanding their skill sets and adapting to emerging AI technologies. The course is updated regularly with new techniques to keep learners up-to-date with the latest developments in the field.
Apart from providing real-world applications and examples, the course also includes interactive demos to facilitate hands-on learning. One of the unique features of Learn Prompting is its non-linear structure, which allows learners to explore the topics that interest them the most. The articles are labeled according to difficulty level, making it easy for learners to find content that suits their level of proficiency. The gradual progression of the material also makes it accessible to those with no technical background, helping them understand even the most advanced prompt engineering concepts.
Learn Prompting is an ideal course for anyone looking to acquire practical, immediately applicable techniques for their own projects.
Highlights of the course include:
- In-depth articles on basic concepts and applied prompt engineering
- Specialized learning chapters for advanced prompt engineering techniques
- An overview of applied prompting using generative AI models
- An inclusive, open-source course for non-technical and advanced learners
- Self-paced learning model with interactive applied prompt engineering demos
- Non-linear learning model designed to make learning relevant, concise, and enjoyable
- Articles rated by difficulty level for ease of learning
- Real-world examples and additional resources for continuous learning
Here's a summary of each chapter:
- Basics: An introductory lesson for learners unfamiliar with machine learning (ML) that covers basic concepts like artificial intelligence (AI), prompting, key terminologies, instructing AI, and types of prompts.
- Intermediate: Focuses on the various methods of prompting and goes into more detail about prompts with different formats and levels of complexity, such as Chain of Thought, Zero-Shot Chain of Thought prompting, and the generated knowledge approach.
- Applied Prompting: Covers the end-to-end prompt engineering process with interactive demos, practical examples using tools like ChatGPT, and solving discussion questions with generative AI. This chapter allows learners to experiment with these tools, test different prompting approaches, compare generated results, and identify patterns.
- Advanced Applications: Covers some advanced applications of prompting that can tackle complex reasoning tasks by searching for information on the internet or other external sources.
- Reliability: Covers techniques for making completions more reliable and implementing checks to ensure that outputs are accurate. It explains simple methods for debiasing prompts, such as using various prompts, self-evaluation of language models, and calibration of language models.
- Image Prompting: Explores the basics of image prompting techniques and provides additional external resources for further learning. It delves into fundamental concepts of image prompting, such as style modifiers, quality boosters, and prompting methods like repetition.
- Prompt Hacking: Covers concepts like prompt injection and prompt leaking and examines potential measures to prevent such leaks. It highlights the importance of understanding these concepts to ensure the security and privacy of the data generated by language models.
- Prompting IDEs: Provides a comprehensive list of various prompt engineering tools, such as GPT-3 Playground, Dyno, Dream Studio, etc. It delves deeper into the features and functions of each tool, giving learners an understanding of the capabilities and limitations of each.
- Resources: Offers comprehensive educational resources for further learning, including links to articles, blogs, practical examples, and tasks of prompt engineering, relevant experts to follow, and a platform to contribute to the course and ask questions.
If you're interested in signing your teen up for the Prompt Engineering Course, please email firstname.lastname@example.org