An AI prompt engineering guide provides instructions and best practices for creating effective and accurate prompts that can be used to generate natural language responses from AI language models.
It includes guidance on prompt format, length, specificity, and diversity.
Here are some tips and advice to help you navigate the world of ai prompt engineering.
Build a Strong Foundation: It’s essential to have a solid foundation in math and science to succeed in engineering.
Make sure you understand the fundamental concepts and principles before moving on to more advanced topics.
Choose a Specialty: Engineering is a broad field, and it’s essential to choose a specialty that interests you. Some popular specializations include mechanical, electrical, civil, and computer engineering.
Get Involved in Projects: Join engineering clubs or organizations and participate in projects that allow you to apply what you’ve learned. This experience will help you build skills and develop problem-solving abilities.
Keep Learning: Engineering is a constantly evolving field, and it’s essential to keep up with the latest advancements and trends. Attend conferences, read technical journals, and continue to learn new skills throughout your career.
Develop Soft Skills: While technical skills are essential, soft skills like communication, teamwork, and leadership are also crucial for success in engineering.
Develop these skills by working on group projects and taking on leadership roles.
Network: Building a professional network can help you find job opportunities, learn about new technologies, and connect with like-minded professionals.
Attend industry events, join professional organizations, and connect with colleagues on social media.
Embrace Diversity: Engineering is a diverse field, and it’s essential to embrace different perspectives and experiences.
Working with people from different backgrounds can lead to innovative solutions and new ideas.
Practice Ethical Behaviour: As an engineer, you have a responsibility to prioritize safety, uphold ethical standards, and protect the environment.
Always consider the impact of your work on society and take steps to mitigate potential risks.
Pursue Professional Development: Continuing education and professional development are crucial for maintaining your skills and staying competitive in the job market. Consider pursuing certifications, advanced degrees, or specialized training programs.
Enjoy the Journey: Engineering can be a challenging and rewarding career, but it’s also important to enjoy the journey.
Take pride in your accomplishments, and don’t be afraid to celebrate your successes along the way. **
Prompt engineering is a highly lucrative skill to learn in 2023, as it can enable you to create millions of dollars’ worth of value with just a few carefully crafted sentences.
In this guide, I will take you step by step through learning this foundational skill, and show you exactly how you can use it to make money and build businesses, even if you have no coding experience.
Prompting involves instructing an AI to perform a task.
We provide the AI with a set of instructions, and it performs the task based on those instructions.
Prompts can vary in complexity, from a phrase to a question to multiple paragraphs worth of text.
The quality of your input, or prompt, directly determines the quality of your output. This is because of a concept called “garbage in, garbage out.”
Prompt engineering is essential because the quality of your prompts determines the value you can extract from large language models like GPT-3.
By constructing effective prompts, you can maximize the potential of these models to yield optimal results on any task.
For example, even simple errors in a prompt can lead to incorrect answers.
However, by tweaking your prompt slightly, you can drastically improve the accuracy of your results.
The core focus of prompt engineering is to make prompts that yield optimal results on any task.
This skill is not limited to coding experts, as anyone can learn it and add it to their toolkit.
By mastering prompt engineering, you can access more opportunities with AI and create substantial value in a variety of fields.
To begin with, it is important to note that the OpenAI Playground is a separate entity from Chat GPT.
For those unfamiliar with the Playground, it offers a flexible platform to interact with all of the OpenAI Suite of products in their natural state, which refers to the form accessible through OpenAI APIs.
This is crucial to understand because anything achievable within the Playground can then be scaled and productized.
Chat GPT, on the other hand, is an application built on top of the GPT-3 model, which can be accessed through the Playground.
However, OpenAI has significantly altered GPT-3 to create Chat GPT through reinforcement learning and fine-tuning, making it distinct from the base model.
Although Chat GPT may be useful on its own, anyone aiming to create value and build scalable businesses needs to learn how to engineer the base models in their natural state.
This is because the base-level models are the only accessible models through the APIs and are the foundation on which new businesses can be built.
Therefore, learning how to engineer prompts for the base models through the Playground is the focus of this video.
We begins by demonstrating a simple prompt that utilizes the power of large language models like GPT-3 to convert a mixed bag of first and last names into last-first order.
The next prompt is crafted to remove personal information from an email using the appropriate placeholder, such as replacing the name “John Doe” with “name.”
This prompt enables the removal of all personally identifiable information, such as email addresses and phone numbers.
Achieving this result within the Playground means that the same prompt can be applied on a larger scale and run through agencies to get the same results.
The important settings that can be adjusted in the sidebar.
The model used to interact with OpenAI can be changed based on the task at hand, as OpenAI has several models for various purposes.
The temperature setting, which determines the randomness of the output, is also crucial, with higher values producing more random results.
On the other hand, a temperature setting of zero produces deterministic outputs. Finally, the max length setting is a crucial part of prompt creation that determines the length of the output.
In conclusion, the OpenAI Playground provides a flexible platform for interacting with the OpenAI Suite of products in their natural state.
By learning how to engineer prompts for the base models through the Playground, one can build scalable businesses.
Adjusting the model, temperature, and max length settings are crucial to prompt creation and can produce varying results. **
When using large language models for AI, there are strict limits on the amount of data that can be used for prompts and responses.
The combined total of prompt and response must be no more than 4,000 tokens, with one token roughly equivalent to four characters in normal English text.
It is important to use the max length setting to ensure the response does not exceed this limit. To avoid repetitive responses or promote new topics, users can adjust settings like frequent penalty or presence penalty.
One method of prompting is called role prompting, where users prompt the AI to assume a specific role, such as a doctor or lawyer. This can be useful when asking legal or medical questions, for example.
By setting the AI in a particular role, users can provide context that helps the AI better understand and answer questions.
For example, a prompt can tell the AI that it is a brilliant mathematician who can solve any problem in the world, and the AI will respond accordingly.
Another method is called shot prompting, which can be divided into three categories: zero shot, one shot, and few shot.
Zero shot prompting allows users to use the AI as an autocomplete engine by asking it a question or providing a phrase without any expected structure.
One shot prompting involves providing a prompt that gives some structure or expectation of how the AI should respond.
Few shot prompting is similar to one shot, but with additional examples provided to help the AI better understand how to respond.
Shot prompting is particularly useful for businesses that want to use AI to answer customer questions or provide support.
By providing a few examples of expected questions and answers, the AI can learn to respond more accurately and efficiently.
When we use big computer programs to make AI work, there are rules about how much information can be used to ask a question and get an answer.
We can only use up to 4,000 small parts of a sentence, which are called tokens.
It’s important to set the AI to not go over this limit. We can also adjust the settings to make sure we get new answers, not the same ones over and over.
One way to ask the AI a question is called role prompting.
This means we tell the AI to act like someone, such as a doctor or lawyer, to help it understand and answer our question better.
For example, if we tell the AI to act like a math genius, it will give us a solution to any math problem we ask.
Another way is called shot prompting. This is when we give the AI a question or a phrase to complete.
There are three types of shot prompting: zero shot, one shot, and few shot. Zero shot means we just give the AI a phrase and it completes it for us.
One shot means we give the AI a little bit more information to help it understand what we’re asking. Few shot is like one shot, but we give the AI a few examples to help it understand even better.
Businesses can use shot prompting to help their customers. By giving the AI some examples of questions and answers, the AI can learn to give better and faster answers to customer questions.
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