Artificial Intelligence (AI), while exciting all the same, is not new. With roots in the 1940s, John McCarthy coined the term in 1956. For many of us, AI brings forth thoughts of robots and apocalyptic film works. I think it is safe to say that we are all hopeful that Hollywood’s imagined relationship with AI never comes to pass. The reality is a rapidly increasing majority of us have or regularly do interact with artificial intelligence. From chatbots on retail sites, to voice recognition on cellular phones AI continuously integrates into our daily lives.
In their 2022 survey NewVantage Partner’s discovered that 91% of the surveyed companies are directly investing in AI. More intriguing is the 20+% jump from 2020 to 2022 in the amount of companies already realizing the benefits of AI investment.
With the 2000 release of the ChatGPT language model system and its astounding ability to converse or write nearly anything, we find ourselves interacting with artificial intelligence in yet another way. Open AI shares “We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.”
ChatGPT is an NLP (natural language processing) AI model. Specifically, it is a large language model that uses the GPT (Generative Pre-trained Transformer) architecture developed by OpenAI. The model has been pre-trained on massive amounts of text data, which allows it to understand and generate human-like language.
A natural language processing (NLP) AI model is an artificial intelligence model that is designed to understand and process human language. It enables computers to understand, interpret, and generate human language, allowing them to interact with humans in a more natural way. Using a combination of machine learning algorithms and natural language processing techniques to understand the context of the conversation NLPs like ChapGPT generate relevant and coherent responses.
As many of us use natural language processing (NLP) models to help us write or research, we find that what we ask, that is how we interact with AI can dramatically change the outcome. It all starts with the prompt.
In the context of AI, prompts refer to the input queries or textual prompts that are used to generate responses from a language model or natural language processing (NLP) model. In other words, prompts are the starting point or the input provided to the AI model, which then generates a response based on its understanding of the prompt.
Prompts can take various forms, including questions, statements, or even incomplete sentences. They can also be tailored to specific domains or topics, such as finance, healthcare, or customer service. The quality of the prompts used can have a significant impact on the performance and accuracy of the AI model.
For example, consider an AI-based language model that has been trained on a large corpus of text data. If a user enters the prompt "What is the capital of France?", the AI model will analyze the text, identify the relevant information, and generate a response such as "The capital of France is Paris."
So here we are, full circle, back to the human touch of prompt engineering.
What is Prompt Engineering?
Prompt engineering is the process of designing and optimizing prompts, or input queries, for a language model or NLP model in order to improve its performance on a specific task. It involves selecting the right words and phrasing for the prompt to help the model understand the context and generate more accurate and relevant responses.
Prompt Engineering Techniques Include:
Selecting relevant keywords: Identifying the most important words or phrases in a prompt that are relevant to the task at hand.
Using templates: Creating pre-defined prompts or templates that are optimized for specific use cases or scenarios.
Adding context: Providing additional context to the prompt to help the model understand the intent and generate more accurate responses.
Fine-tuning the model: Adjusting the weights and parameters of the model to better fit the specific prompt and task.
Evaluating and iterating: Testing and evaluating the performance of the model with different prompts, and making adjustments to improve its accuracy and efficiency.
So, What’s the Value Add?
One of the primary benefits of prompt engineering is that it can help to ensure that an AI model generates high-quality responses and that is the ultimate goal. By carefully crafting prompts that are specific, relevant, and concise, developers can create models that are more likely to produce accurate and useful results.
In addition, prompt engineering can also help to improve the efficiency of an AI model. By designing prompts that are optimized for the specific task at hand, developers can reduce the amount of data and computing power required to generate responses.
Getting a relevant and useful piece of content on the first try rather than repeating the process is an advantage. If you have used NLP technology for personal use, you already know that how you phrase your initial prompt can reduce the amount of editing required and thus the time to completion. This can make AI models more cost-effective and scalable.
Furthermore, prompt engineering can also improve the interpretability of AI models. By designing prompts that provide insight into the reasoning and decision-making processes of the model, developers can make it easier for humans to understand how the model works and why it generates certain responses.
Artificial Intelligence is a part of modern everyday life and learning to use it to our advantage, rather than as another time consuming tool can make all the difference. Prompt engineering is the starting point and an essential tool for creating effective AI models. By carefully crafting prompts, you can improve the quality, efficiency, and interpretability of their models, making them more useful and functional.
More on AI
While prompt models are a great place to start discussions for NLP technology, it is but a segment of artificial intelligence. As a part of an ongoing discussion, we look forward to sharing insight on AI Bias, AI in Cybersecurity, and more. Stay tuned to learn who is who in this ever growing field and how a career in information technology can lead to working with future driving AI platforms.