"Generative AI refers to a subset of artificial intelligence techniques focused on creating new content, such as images, text, audio, or other forms of data, that is original and not directly copied from existing examples. Unlike traditional AI systems, which are often used for classification, prediction, or optimization tasks, generative AI models are trained to generate new data that is similar to the training data but not identical to it. These models are typically based on deep learning architectures, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), and they can be used for a wide range of applications, including creative design, content creation, data augmentation, and simulation."

"Artificial intelligence designed to produce output, esp. text or images, previously thought to require human intelligence, typically by using machine learning to extrapolate from large collections of data; (also) a system, piece of software, etc., used to create content in this way; abbreviated generative AI."

A machine learning model within the AI field trained on specific data to recognize patterns and organization and use that learning to create high-quality, original content such as text, images, etc.  Generative AI does not understand the content it generates.

"AI hallucination refers to an occurrence where a generative AI model produces outputs that are distorted, unrealistic, or nonsensical, often due to limitations or biases in the training data or the underlying algorithms. These hallucinations can manifest in various forms depending on the type of data the AI model is trained on, such as images, text, or audio.

In the context of image generation, AI hallucinations may result in the generation of images that contain bizarre or impossible features, artifacts, or structures that do not correspond to reality. Similarly, in text generation, AI hallucinations might lead to the production of nonsensical or incoherent sentences or paragraphs.

AI hallucinations can occur when the model is pushed beyond its training data or when it encounters patterns or inputs that it has not been trained to handle effectively. While AI hallucinations can sometimes produce entertaining or intriguing outputs, they also highlight the need for careful validation and testing of AI models to ensure that they generate reliable and realistic results."

"Large Language Models (LLMs) refer to a class of artificial intelligence models that are trained on massive datasets to understand and generate human-like text. These models, such as OpenAI's GPT (Generative Pre-trained Transformer) series and Google's BERT (Bidirectional Encoder Representations from Transformers), consist of deep neural networks with millions or even billions of parameters.

LLMs have revolutionized natural language processing tasks by demonstrating remarkable capabilities in understanding and generating text across various domains and languages. They can perform tasks such as language translation, text summarization, sentiment analysis, question answering, and more, often achieving state-of-the-art performance.

The training process for LLMs typically involves unsupervised pre-training on large corpora of text data followed by fine-tuning on specific tasks with labeled data. These models rely on the Transformer architecture, which enables them to capture long-range dependencies in text and generate coherent and contextually relevant responses.

Despite their impressive capabilities, LLMs also raise ethical concerns related to potential misuse, bias in generated text, and the impact on job displacement. As such, research in the field often focuses on improving model robustness, fairness, and transparency."

 "A language model that utilizes deep (see DEEP entry 1 sense 8) methods on an extremely large data set as a basis for predicting and constructing natural-sounding text."

Computer algorithms that use large datasets to learn and recreate language patterns.

"Prompt engineering refers to the process of designing or crafting specific input prompts to guide the behavior of large language models (LLMs) towards desired outputs. In the context of LLMs like GPT (Generative Pre-trained Transformer) models, which generate text based on input prompts, prompt engineering involves formulating prompts that elicit responses aligned with the intended task or objective.

Prompt engineering can significantly influence the performance and behavior of LLMs, allowing users to steer the model towards generating text that meets certain criteria, such as sentiment, style, topic, or level of detail. By carefully constructing prompts, researchers and developers can fine-tune the output of LLMs to better suit particular applications or use cases.

Effective prompt engineering requires an understanding of the underlying mechanisms of LLMs and how they interpret and generate text based on input prompts. This may involve experimentation with different prompt formats, lengths, and wording to achieve the desired results.

Prompt engineering is particularly relevant in applications such as text generation, question answering, summarization, and dialogue systems, where controlling the output of LLMs is crucial for achieving high-quality results."

The process of creating prompts for generative AI using detailed instructions that could include text, images, examples, and suggested outputs.  The better the prompt created the better the results.