Mastering GenAI: Your Essential AI Glossary for Communication Professionals

Artificial intelligence (AI), particularly generative AI (GenAI), is transforming how professionals work, innovate, and solve problems across various industries. Mastering GenAI's potential can feel like finding that key puzzle piece - unlocking new opportunities and filling in the gaps where traditional approaches fall short. For communication professionals, understanding how these technologies fit into the larger picture (and most importantly, how they can be applied) is no longer optional.

Whether you're crafting messaging, generating new content, or building brand strategies, AI, and particularly generative AI and large language models (LLMs), are becoming essential tools that can complete your professional toolkit.

That’s where this article comes in.

If you’ve been overwhelmed by the technical jargon and unsure where to start, you're not alone. This glossary was carefully curated and designed to fit right where you need it. As I've been exploring the deep rabbit hole that is GenAI, I've assembled a comprehensive list of key AI terms, specifically chosen for communication professionals like you - though if I'm being honest, this glossary is useful for anyone trying to make sense of it all.

This guide offers definitions, organized into themes, to help grasp the full picture while diving into the practicalities of using AI effectively in our work.

I've explore how concepts like fine-tuning, zero-shot learning, and prompt engineering come into play when you’re working with AI tools to generate content or manage customer interactions. I've also considered terms that touch on ethical considerations to keep in mind and how to navigate potential challenges, like AI bias or hallucinations in generated content.

The goal here is simple: to provide a friendly, professional, and easy-to-understand resource that will empower anyone to embrace AI and use it to enhance your communication strategies.

Whether you’re already familiar with some of these concepts or are entirely new to the world of AI, this glossary will serve as a practical guide to help you piece together your knowledge and hopefully let you stay ahead in a rapidly evolving field.

If you believe I've omitted some important definitions, please let me know in the comments. This is a work-in-progress - one that I plan to revisit again and again as my knowledge and understanding of GenAI (and its use) expands.

Basic definitions and concepts

1. Artificial Intelligence (AI) - The field of computer science focused on creating systems capable of performing tasks that require human intelligence, such as understanding language, recognizing patterns, and making decisions.
2. Generative AI - A subset of AI that generates new content (text, images, audio, etc.) based on learned patterns from existing data. Examples include tools like GPT (text generation), DALL-E (image generation), and more.
3. Large Language Models (LLMs) - A type of AI model trained on vast datasets to understand and generate human language. They can perform a variety of tasks, such as answering questions, writing content, and summarizing information. Examples include GPT-3, GPT-4, and similar models.
4. Prompt & Prompt Engineering - Prompts are the input text provided by a user to a generative AI or language model. The quality of a prompt significantly impacts the AI's output, making prompt engineering an important skill. Prompt Engineering is the skill of crafting inputs to get the most useful and relevant responses from generative AI. For communication professionals, this is a practical ability that directly impacts the quality of AI-generated content.
5. Text Completion - A common task performed by LLMs, where the model predicts and generates the next portion of text based on a given input. This functionality underlies many generative AI applications, including chatbots, copywriting tools, and code assistants.
6. GPT (Generative Pre-trained Transformer) - One of the most popular types of large language models, responsible for generating text from input prompts. Familiarity with GPT and how it works is essential for using AI tools like ChatGPT or OpenAI's APIs.
7. Chatbot - An AI-powered tool designed to simulate human conversation. Chatbots can perform various tasks such as answering questions, helping with customer service, or automating tasks in real-time.
8. Hallucination in AI - The phenomenon where AI models generate information or responses that appear factual but are not based on real data. Communication professionals must be aware of this to ensure accuracy in AI-generated content.
9. Bias in AI - The inherent biases in AI models resulting from the datasets they are trained on. Bias can lead to unintended or unfair outcomes in AI-generated content. Understanding and mitigating bias is important when using generative AI.
10. Ethics in AI - The consideration of moral issues related to AI’s impact on society, including bias, fairness, privacy, and transparency. Communication professionals need to be aware of ethical AI use when adopting generative tools in their work.

AI Training and Learning Techniques

1. Backpropagation - A process in AI training where the model updates its parameters to reduce errors in its predictions. Understanding how models learn through backpropagation can be useful when explaining AI behavior or performance improvements.
2. Distillation - A technique where a smaller, simpler model is trained to replicate the behavior of a larger, more complex model. Distilled models are more efficient and faster, allowing organizations to deploy AI tools without requiring massive computational resources.
3. Ethical AI - An approach to AI development that ensures fairness, transparency, and responsibility in AI usage. For communication professionals, understanding ethical AI principles is essential when deploying AI tools that interact with the public.
4. Few-shot Learning - The capacity of AI models to learn new tasks from just a few examples, improving their ability to adapt to specific tasks with minimal additional training. It's typically used to train models for classification tasks when suitable training data is scarce.
5. Federated Learning - A type of machine learning where the model is trained across decentralized devices without exchanging data between them. This can be relevant for privacy-sensitive applications of AI, where data cannot be centrally stored. Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized.
6. Fine-tuning - The process of training a pre-trained AI model on a specific dataset to optimize its performance on particular tasks, such as domain-specific content generation or answering niche questions.
7. Gradient Descent - A method used to optimize machine learning models by iteratively adjusting model parameters to minimize a loss function. It plays a crucial role in training AI models, including large language models, by improving accuracy over time.
8. Human-AI Collaboration - The process where humans and AI work together to achieve better outcomes than either could independently. In communication, this might involve using AI to generate first drafts or ideas, while human experts refine the content for accuracy and tone.
9. Human-in-the-loop (HITL) - A system design where humans are involved in the AI process to provide oversight, corrections, or guidance. In content generation, human editors often review and refine AI-generated content, ensuring high quality and alignment with brand values.
10. Language Model Pre-training - The initial phase of training LLMs, where models are trained on large datasets to learn general language patterns. After pre-training, models are fine-tuned for specific tasks or applications.
11. Model Drift - A phenomenon where an AI model's performance degrades over time due to changes in the underlying data distribution it was trained on. As communication environments, language use, and datasets evolve, communication professionals should be aware of model drift to ensure that AI-generated content remains relevant, accurate, and aligned with current trends. Regular model updates and fine-tuning help mitigate this issue.
12. Pretraining Task - The task a model is initially trained on before being fine-tuned for specific tasks. For example, GPT models are pretrained on predicting the next word in a sentence, which gives them the foundational ability to generate coherent text.
13. Reinforcement Learning from Human Feedback (RLHF) - A technique used to fine-tune models using feedback from human users. It allows LLMs to improve over time based on real-world usage, aligning their outputs more closely with human preferences and expectations.
14. Self-supervised Learning - A training method where models learn from unlabeled data by predicting parts of the data based on other parts. Most large language models, like GPT, are trained using self-supervised learning.
15. Transfer Learning - A technique where a model trained on one task is adapted to another, leveraging previously learned knowledge. For instance, a pre-trained LLM can be fine-tuned to write marketing content, even though it was originally trained on a broad dataset.
16. Turing Test - A test of a machine’s ability to exhibit human-like intelligence. An AI passes the Turing Test if it can generate responses indistinguishable from those a human would provide.

AI Architectures and Models

1. AI APIs - Interfaces that provide access to advanced artificial intelligence models capable of creating new, original content based on learned patterns from existing data. Communication professionals can leverage these APIs for content generation, chatbots, or automating writing tasks.
2. API (Application Programming Interface) - A set of protocols and tools for building software applications. APIs allow communication professionals to integrate generative AI models into platforms like websites, customer service systems, and content management systems.
3. Artificial General Intelligence (AGI) - A hypothetical form of AI that possesses the ability to understand, learn, and apply intelligence to any problem, much like a human. It is an aspirational goal of AI development, beyond current AI capabilities.
4. Autoregressive Models - A class of models where the output at each step is used as input for the next step. GPT models are autoregressive, as they generate text one token at a time based on previously generated tokens.
5. Autoregressive Decoding - A technique used in generative AI models where the model generates text one token at a time, each time conditioning on previously generated tokens. This process underlies how tools like GPT write coherent paragraphs or articles.
6. BERT (Bidirectional Encoder Representations from Transformers) - A pre-trained language model developed by Google that is commonly used for tasks like question-answering, classification, and summarization. While GPT is generative, BERT is more focused on understanding language context and meaning.
7. Conversational AI - AI systems designed to engage in meaningful and contextually relevant conversations with humans. LLMs, chatbots, and virtual assistants all fall under this category and are key tools for customer support or marketing communications.
8. Deep Learning - A subset of machine learning that uses neural networks with many layers (deep neural networks) to model complex patterns in data. Large language models like GPT are built using deep learning techniques.
9. Fine-tuned Models vs. Base Models - Base models are large pre-trained models like GPT-3 or GPT-4, whereas fine-tuned models have undergone additional training on specific datasets to specialize them for particular tasks (e.g., legal text generation, customer service responses).
10. Generative Adversarial Network (GAN) - A type of AI model consisting of two networks (a generator and a discriminator) that work together to create realistic synthetic data. While GANs are more commonly associated with image generation, the underlying concepts of generative and adversarial learning influence advancements in other AI models.
11. Hyperparameters - Settings or configurations used to control the training process of an AI model. Unlike parameters, hyperparameters are not learned from data but set before training (e.g., learning rate, batch size). They impact model performance and training efficiency.
12. Multilingual Models - Language models trained to understand and generate text in multiple languages. These are especially important for global communication strategies, where messages need to be tailored for different linguistic audiences.
13. Multimodal AI - AI models that can process and generate multiple types of data, such as text, images, and audio, allowing for more sophisticated applications. For instance, DALL-E can generate images from text prompts, making it a multimodal model.
14. Natural Language Generation (NLG) - A branch of AI focused on generating human-readable text from structured or unstructured data. It is commonly used in LLMs to produce coherent and contextually appropriate text.
15. Natural Language Processing (NLP) - A field of AI that focuses on the interaction between computers and human languages. It involves enabling machines to understand, interpret, and generate human language in a way that is meaningful.
16. Parameter - A variable in an AI model that is learned during training and determines how the model processes data. The number of parameters in a model (e.g., OpenAI’s GPT-3 has 175 billion) typically correlates with the model’s complexity and capability.
17. Perplexity - A metric used to evaluate the performance of a language model. Lower perplexity indicates that the model is better at predicting or generating text sequences, which is crucial in understanding the quality of LLM outputs.
18. Pre-trained Models - Models that have already been trained on large datasets and can be fine-tuned for specific tasks. Pre-trained models allow for faster deployment in real-world applications, as they already possess foundational knowledge.
19. Sequence-to-Sequence Models - A type of model used in tasks where the input is a sequence and the output is another sequence, such as language translation or text summarization. These models are important in content generation workflows.
20. Stochasticity in AI - Refers to the random elements introduced in AI models, especially in generative models. For example, adding randomness in token selection during text generation can lead to more diverse and creative outputs.
21. Synthetic Data - Artificially generated data that can be used to train AI models. Generative AI can create synthetic data to help models learn more effectively in situations where real-world data is limited or sensitive.
22. Temperature (in AI Models) - A parameter that controls the randomness of predictions made by language models. Lower temperature results in more deterministic outputs, while higher temperature leads to more diverse and creative responses. Adjusting the temperature helps tune the style of generated text.
23. Tokenization - The process of breaking down text into smaller units (tokens), such as words or characters, for processing by LLMs. LLMs often process tokens rather than full sentences, impacting the length and complexity of input they can handle.
24. Transformer Architecture - Transformers are a type of neural network architecture that transforms or changes an input sequence into an output sequence. They do this by learning context and tracking relationships between sequence components.
25. Zero-shot Learning - The ability of AI models to perform tasks they were not explicitly trained for, based purely on generalized patterns they’ve learned. For example, an LLM might be able to answer a new type of question it has never encountered before.

AI Functionality and Mechanisms

1. Attention Mechanism - A key component of transformer-based models (such as GPT), it allows models to focus on specific parts of the input data. This mechanism enables LLMs to better understand context and relationships between words or phrases in a sentence. It improves the performance of models by focusing on relevant information.
2. Beam Search - A search algorithm used in natural language processing to generate text by considering multiple possible sequences of words at each step and choosing the most likely one. It helps improve the quality of AI-generated text by balancing exploration and exploitation of options. It is a modification of best-first search that reduces its memory requirements.
3. Context Window - The amount of text an LLM can process at once. Larger models typically have larger context windows, allowing them to handle more complex prompts or longer inputs. The positional encoding in generative AI that determines token placement within that textual sequence. The context window size is the number of tokens both preceding and following a specific word or character (target token) and it determines the boundaries within which AI remains effective.
4. Embedding - A representation of text or data in vector form that allows AI models to perform operations like similarity searches, clustering, or classification. Embeddings are crucial in LLMs for understanding relationships between words.
5. Explainability in AI - The ability to understand and explain how an AI model arrives at its decisions or outputs. As generative AI becomes more prevalent, explainability helps build trust and transparency in its use.
6. Latent Space - A mathematical space where AI models, especially generative ones, represent data. Latent spaces allow models to find relationships between different concepts and generate new combinations of these elements, which is critical in generative AI outputs.
7. Loss Function - A mathematical function used to measure how well a machine learning model performs by comparing predicted outputs to actual values. During training, the model learns to minimize this loss to improve its performance on tasks like text generation.
8. Neural Network - A system of interconnected nodes (neurons) inspired by the human brain, used in machine learning models to process data and identify patterns. Deep learning models, including LLMs, are based on neural networks.
9. Overfitting – A scenario where a model becomes too specialized to the training data, performing poorly on new, unseen data. Communication professionals should be aware of overfitting to ensure that AI models used in content generation or strategy development remain flexible, adaptable, and applicable to broader, real-world situations, rather than being overly tailored to specific datasets.

Source Material and References

Below is a short list of sites where you may further explore each of these concepts and where some of these definitions were sourced.

• IBM: AI and Machine Learning
• Lark Topics: AI Glossary
• OpenTrain AI: AI Glossary
• Coursera: AI and Machine Learning
• AWS: What is Artificial Intelligence (AI)?
• Mostly.ai: What is synthetic data
• TechTarget: What is?