What Is Prompt Engineering In Ai? How Can It Make Me Money? (Part 1)
The matter we are going to handle today is related to the concept of “Artificial Intelligence”. What is Prompt Engineering in AI and how smart can it be to learn it?
Artificial intelligence (AI) is a broad term that refers to the ability of machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision making, and creativity.
Key Takeaways
| AI prompting is a technique to communicate with generative AI models using natural language text. |
| Prompt engineering is the skill of crafting effective and precise prompts that elicit the desired output from the model |
| AI prompts can be used to guide the model to perform various tasks, such as writing, generating images, answering questions, and more. |
| Prompt engineering can help you leverage the power and potential of generative AI for various purposes, such as creativity, productivity, education, and entertainment. |
| Prompt engineering involves understanding the capabilities and limitations of the model, providing enough context and instructions, and choosing the appropriate tone and format for the output. |
With that said, let´s start from the top! We can divide AI into two main categories: narrow AI and general AI.
The Differences Between Narrow AI And Generative AI
Narrow AI
Is the type of AI that can perform specific tasks very well (such as playing chess, recognizing faces, or translating languages). Narrow AI is based on predefined rules and algorithms that are designed for a particular domain or problem.
General AI is the type of AI that can perform any task that a human can do across different domains and contexts.
“Real” General AI is not yet achieved, but it is the ultimate goal of many AI researchers and enthusiasts. To ideally achieved it, General AI would require machines to have common sense, self-awareness, and general knowledge.
Generative AI
Is the type of AI that can instead perform any task that a human can do across different domains and contexts.
In other words, we could simplistically define Generative AI as “a branch of AI that focuses on creating new content or data that did not exist before, such as text, images, music, or code.”
How does it works? Generative AI models are trained on large amounts of data to learn the patterns and structures of the data domain. Then, they can generate new data based on the input they receive from the user or from another source.
The training models are often based on deep neural networks, which are complex mathematical models that mimic the structure and function of the human brain. Deep neural networks consist of multiple layers of interconnected units called neurons that process information and learn from data.
Large Language Models (LLMs)
One of the most popular types of generative AI models nowadays is “Large Language Models” (LLMs), which are trained on massive amounts of text data to learn the rules and nuances of natural language.
As such, LLMs can generate coherent and fluent text based on a given input, such as a word, a phrase, a sentence, or a paragraph.
Do something come into your mind right now? 🙂
Yes, you are right : Some popular examples of Large Language Models are ChatGPT, Google´s Bert, facebook´s Bart and Bing´s Chat.
Perhaps, you have already experienced how these models have shown impressive results in various natural language processing (NLP) tasks, such as text summarization, text generation, question answering, sentiment analysis, and more…
Or maybe not yet…Man, where have you been in these last years?? Alright, check out here for a quick comparison between for instance ChatGPT and Google´s Bert
However, generative AI models are not perfect. They have limitations and challenges that we will try to address here : one of the main challenges is how to communicate with them effectively and efficiently. How can we tell them what we want them to do and how we want them to do it?
How can we ensure that they produce relevant and accurate outputs that meet our expectations and needs?
This is where AI prompting comes in!
What Is AI Prompting?
AI prompting is a technique to communicate with generative AI models using natural language text. A “prompt” is a natural language text that describes the task that an AI model should perform. A prompt can be a query, such as “what is Pitagora’s theorem?”, a command, such as “write a poem about leaves falling”, a feedback, such as “too verbose”, or even a combination of these elements.
Therefore, a prompt serves as a guide for the model to generate an output that matches the user’s intention and goal. The output can be text, image, audio, video, or any other type of data that the model can produce.
(here is The New York Times with his take on how to use AI to generate Images)
But the whole subject of AI prompting is not just about writing prompts. It also involves understanding how generative AI models work and what they can and cannot do.
At a more professional level, it requires knowledge of natural language processing (NLP) and machine learning (ML) concepts and techniques. It also requires creativity and analytical thinking skills.
The skill of crafting effective and precise prompts that elicit the desired output from generative AI models is called Prompt-Engineering.
What Is Prompt Engineering?
Prompt engineering is the process of designing and developing prompts that effectively utilize the capabilities of that specific generative AI model for various purposes.
Prompt engineering aims to optimize the quality and diversity of the generated outputs while minimizing potential errors and biases.
Prompt engineering involves several steps. Here are the most obvious ones:
- Defining the goal: What do you want the model to do? What is the purpose of your prompt? What type of output do you expect?
- Choosing the model: Which generative AI model suits your goal best? What are its strengths and weaknesses? How does it handle different types of inputs and outputs?
- Writing the prompt: How can you phrase your prompt in a clear and concise way? How can you provide enough context and instructions for the model to understand your task? How can you choose the appropriate tone and format for your output?
- Evaluating the output: How can you measure the quality and relevance of the generated output? How can you check for errors and biases? How can you provide feedback and improve your prompt?
So, Prompt engineering is not a one-time process. It is an iterative and experimental process that requires trial and error, testing and refining, and constant learning and improvement.
Prompt engineering is not a fixed set of rules or formulas either… It is a new, flexible and creative art that depends on various factors, such as the model, the task, the data, the user, and the context.
And finally, Prompt engineering is not meant to be a solo activity : It should be a collaborative and interdisciplinary endeavor, and as such, should involve communication and interaction with other prompt engineers, AI researchers, domain experts, and end users.
Why Is Prompt Engineering Important?
Prompt engineering has an incredible value potential since it can help you leverage the power and potential of generative AI for various purposes, such as:
- Creativity: You can use prompt engineering to generate new and original content or data you can use professionally or just for entertaining purposes. For example, you can use prompt engineering to better formulate your own ideas, but also write stories, poems, jokes, songs, or even pure code.
- Productivity: You can use prompt engineering to automate or simplify tasks that are tedious or time-consuming. For example, you can use prompt engineering to summarize documents, generate reports, create presentations, or write emails.
- Education: You can use prompt engineering to learn new things or improve your skills. For example, you can use prompt engineering to answer questions, explain concepts, provide examples, or give feedback.
- Entertainment: You can use prompt engineering to have fun or relax. For example, you can use prompt engineering to play games, chat with friends, watch videos, or listen to music.
Why Is Prompt Engineering Important For Me?
Any of the above single purposes have generated a multi-milion Industry over the years. And with the advent of the Internet, the progress in the relative applied technologies has skyrocketed off the charts…
What I am trying to tell you right now is :
| Here is a pretty new and very concrete opportunity to carve for yourself an interesting and very, very remunerative profession, if you choose to invest time and effort to learn the particular skills that are appropriate for your industry of choice! |
(And Appropó : Prompt engineering is also a great way to understand the capabilities and limitations of generative AI models better. By writing prompts and evaluating outputs, you can learn how generative AI models work and what they can and cannot do. From there, you can also discover and implement new ways to interact with them and improve them)..
Ok, ok, you want some more precise data, right? I´ve got you served : here is an article I wrote with some precise detail about the typical income of a Prompt Engineer in October 2023. Enjoy!
(…and why not check out some more ways of making money online? Yes, we have some good ideas to offer about interesting side jobs too!! Or maybe you set yourself to become a professional web developer? Great choice! On this website there is plenty of infos to start your digital career TODAY!
Conclusion
In this article, we have introduced the concept of AI prompting and its importance for communicating with generative AI models. We have also explained what prompt engineering is and why it is a valuable skill for leveraging the power and potential of generative AI for various purposes.
We also covered the topic of how this new discipline is about to become a real gold mine in the years to come.
In the next part of this article, we will explore some of the techniques and tools for prompt engineering. We will also provide some examples of prompts for different tasks and models. Stay tuned!
