Every organization, big and small, has an opportunity thanks to the emergence of generative artificial intelligence (AI). Generative AI tools like ChatGPT and others are in a position to begin to help organizations reduce costs, automate manual and repetitive tasks, and gain efficiencies across each technology stack.
What is generative AI, though, if you need the proper tools to leverage its power? Enter prompt engineering.
What is Prompt Engineering?
The concept is not new, but it has gained momentum in a way the field has never seen before. Since machine learning and AI began to pick up steam five or so years ago, prompt engineering has risen in prominence. What is it, though, and how is it different from other types of engineering?
Prompt engineering is all about getting the most out of AI systems, incredibly generative AI systems. With any AI system, you want to get the absolute perfect response out of it, but how do you extract the intelligence embedded within? You prompt it and train it step by step to give you the most accurate and helpful response possible.
Prompt engineering revolves around crafting questions and comments to AI systems that net the most promising results. Organizations are in a race for talent, as well as for technology, to take advantage of what generative AI enables.
The Key Concepts of Prompt Engineering
Prompt engineering is based on several concepts and principles that build upon each other. These include prompting the AI, training the AI, maintaining a library of prompts, and categorizing prompts and their associated models.
With AI prompting, the concept here revolves around knowing the right questions for the AI system.
Let us take the example of a situation where we ask an AI system what two numbers are equal when put together. Without the best prompting, an AI system could interpret this in many ways. It may add the numbers, multiply them, and put them side by side. Prompt engineering can help the AI system to clarify the question so that it can come up with an accurate solution once it knows the actual ask.
An AI system is only as good as the data it has to work off of. The AI system may know the question and understand it, but does it have the dataset behind it to give the correct response? AI systems need to learn over time and enhance their output.
Prompt engineering needs to work to provide prompting and learning opportunities for the AI system to learn, sitting on top of the database or model running behind the scenes.
Library of Prompts
Prompt engineering will create a library of prompts to feed the AI system, but once the prompts are designed, how do you maintain them? Prompts will need to be updated significantly over time as AI systems either upgrade themselves or are entirely replaced with new technology. Prompt engineering is not only about creating prompts but also about maintaining those prompts to ensure that they continue to perform iteratively better day after day.
Prompts Specific to Models
Not all prompts are going to work properly for every type of model. You need to test, validate, and then bucket the prompts so that they can interact with different AI models most effectively.
Importance of Prompt Engineering in Natural Language Processing (NLP) and Artificial Intelligence
Natural language processing (NLP) and artificial intelligence are all about allowing AI systems to understand the meaning behind text, video, audio, or other content formats. A model may have tons of information behind it, with endless data it can pull from and calculate outputs around. The AI system, though, needs to understand the question presented in the text. Everyone asks questions differently, so prompt engineering can help an AI system naturally process language, allowing users to narrow in on the valid ask.
Applications of Prompt Engineering
Prompt engineering has endless possibilities in terms of organizational power. Organizations need to understand the power that prompt engineering has first and foremost. This means identifying the value and then recruiting talent that can help make that value a reality.
Some of the more common applications of prompt engineering include implementing a chatbot for customer support or automating an internal IT help desk. Prompt engineers would work to design the systems so that the customers will feed the system the correct information, and the AI system will be able to take that information and use it effectively.
Prompt development is the setup of tasks for the AI with quick commands. To your code, it’s possible to use predefined commands that send prompts to the LLM (Large Language Model), and better, you can reuse these tasks you have previously created.
The highlight is the time saved by the developer when working with AI. With the help of artificial intelligence, they can use ready-made models to suggest the best options for what they want to do. The result is agility, efficiency, and quality.
Risks Associated with prompting and solutions
Risks are present when it comes to prompt engineering. Prompt engineers can help an AI system ask the right questions for a more accurate answer, but it can also work oppositely. What if prompt engineering is done improperly? The inverse of its goal may persist, where users get incorrect answers (and, worst, don’t get that the answer is wrong).
The best solution for this is a sound, prompt engineering process where the goal is defined and then iteratively built with continuous testing, refinement, and acceptance of results before implementation.
The power of prompt engineers is accurate, as they hold the keys to unlocking what AI systems can do for organizations. As a leader, it is time to take prompt engineering seriously to unlock its true potential.
Take action today! Talk to our team of experts and continue reading the information we have made available to understand prompt engineering and act on what it can reveal.
Comment below your takeaways from this article and how you plan to apply the learnings in the future!