Definitions of AI Terms: A Comprehensive AI Glossary

Understanding AI terminology is essential for navigating today’s rapidly evolving technology landscape. Whether you’re implementing your first AI solution or scaling existing systems, this AI glossary provides clear, practical definitions of the terms you’ll encounter. At Far Horizons, we believe that systematic innovation starts with a solid foundation—and that foundation is built on understanding the language of AI.

This machine learning glossary is designed to demystify AI terminology and help you make informed decisions about technology adoption. We’ve organized these AI definitions alphabetically for easy reference, with practical context for each term.

A

AI (Artificial Intelligence)

Computer systems designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Modern AI encompasses everything from narrow, task-specific systems to emerging general-purpose capabilities. When evaluating AI solutions, focus on measurable outcomes rather than technology hype—our LLM Residency program helps organizations separate AI potential from AI reality.

Agent

An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike simple automation, agents can adapt their behavior based on feedback and changing conditions. Examples include chatbots that handle customer service, automated trading systems, or AI assistants that manage scheduling.

Algorithm

A step-by-step procedure or formula for solving a problem or completing a task. In AI, algorithms process data to identify patterns, make predictions, or generate outputs. The quality of your algorithm directly impacts your results—which is why systematic validation is crucial before production deployment.

Attention Mechanism

A technique that allows AI models to focus on relevant parts of input data when generating outputs, similar to how humans pay attention to specific words when reading. This breakthrough innovation enables models to handle long sequences of text and understand context effectively. Attention mechanisms are fundamental to transformer architectures and modern LLMs.

Augmented Intelligence

An approach that emphasizes AI as a tool to enhance human capabilities rather than replace them. This philosophy aligns with practical enterprise AI adoption—technology should amplify your team’s expertise, not substitute it.

B

Bias (AI Bias)

Systematic errors in AI outputs that reflect prejudices in training data or algorithm design. AI bias can lead to unfair outcomes in hiring, lending, or criminal justice applications. Identifying and mitigating bias is a critical component of responsible AI governance—our assessment frameworks include comprehensive bias evaluation protocols.

BERT (Bidirectional Encoder Representations from Transformers)

A transformer-based model developed by Google that reads text bidirectionally to better understand context. BERT revolutionized natural language processing by achieving state-of-the-art results on multiple language tasks. It’s particularly effective for search optimization, question answering, and text classification.

C

ChatGPT

OpenAI’s conversational AI system based on GPT architecture, which popularized LLM technology for mainstream users. ChatGPT demonstrated that large language models could engage in coherent, contextually aware conversations across diverse topics.

Classification

The process of categorizing data into predefined groups or classes. For example, email spam detection classifies messages as “spam” or “not spam,” while sentiment analysis classifies text as positive, negative, or neutral. Classification is one of the most common machine learning tasks in business applications.

Computer Vision

AI systems that can interpret and understand visual information from the world, including images and videos. Applications range from facial recognition and autonomous vehicles to quality control in manufacturing and medical image analysis.

Convolutional Neural Network (CNN)

A specialized neural network architecture designed for processing grid-like data, particularly images. CNNs automatically learn to detect features like edges, textures, and shapes through multiple layers of processing. They power most modern computer vision applications.

Context Window

The amount of text an LLM can process at once, measured in tokens. Larger context windows allow models to maintain coherence over longer conversations and documents. Understanding context window limitations is essential for designing effective AI workflows—our teams help clients architect solutions that work within these constraints.

D

Data Augmentation

Techniques for artificially expanding training datasets by creating modified versions of existing data. For example, rotating, flipping, or cropping images to generate additional training examples. Data augmentation helps models generalize better and reduces overfitting.

Deep Learning

A subset of machine learning using neural networks with multiple layers (hence “deep”) to learn hierarchical representations of data. Deep learning powers most modern AI breakthroughs, from image recognition to natural language processing. However, these systems require significant computational resources and careful architecture design.

Diffusion Model

A generative AI technique that creates new content by learning to reverse a gradual noise-adding process. Diffusion models power image generation systems like DALL-E and Stable Diffusion, producing remarkably realistic and creative outputs.

E

Embedding

A mathematical representation of data (words, images, or other content) as vectors in high-dimensional space, where similar items are positioned close together. Embeddings enable AI systems to understand relationships and similarities between different pieces of content. They’re fundamental to semantic search, recommendation systems, and RAG implementations.

Epoch

One complete pass through the entire training dataset during the model training process. Training typically involves multiple epochs, allowing the model to gradually improve its performance through repeated exposure to the data.

Explainable AI (XAI)

AI systems designed to provide human-understandable explanations for their decisions and predictions. As AI moves into regulated industries and high-stakes applications, explainability becomes crucial for trust, compliance, and debugging. Our governance frameworks prioritize explainability from day one.

F

Fine-Tuning

The process of taking a pre-trained model and adapting it to perform specific tasks by training it on specialized data. Fine-tuning is more efficient than training from scratch and often delivers better results for domain-specific applications. It’s a key technique in our LLM Residency sprints.

Foundation Model

Large-scale AI models trained on broad data that can be adapted for various downstream tasks. Examples include GPT-4, Claude, and BERT. Foundation models represent a paradigm shift—instead of building task-specific models from scratch, organizations can leverage pre-trained capabilities and customize through fine-tuning or prompt engineering.

G

Generative AI

AI systems that create new content—text, images, code, music, or video—rather than simply analyzing existing data. Generative AI has transformed creative workflows, software development, and content production. However, systematic validation is essential to ensure outputs meet quality and accuracy standards.

GPT (Generative Pre-trained Transformer)

A family of large language models developed by OpenAI that use transformer architecture and are pre-trained on massive text datasets. GPT models can generate human-like text, answer questions, write code, and perform various language tasks through few-shot or zero-shot learning.

Gradient Descent

An optimization algorithm that iteratively adjusts model parameters to minimize error by moving in the direction of steepest descent. Gradient descent is fundamental to training neural networks, though the mathematics remain invisible to most users—what matters is understanding when models converge to useful solutions versus when they need architectural changes.

H

Hallucination

When AI models generate plausible-sounding but factually incorrect or nonsensical information. LLM hallucinations are a significant challenge for enterprise applications requiring accuracy. Mitigation strategies include retrieval-augmented generation (RAG), systematic fact-checking workflows, and appropriate human oversight.

Hyperparameter

Configuration settings that control the learning process but aren’t learned from data—for example, learning rate, batch size, or number of layers. Tuning hyperparameters is part art, part science, and significantly impacts model performance.

I

Inference

The process of using a trained AI model to make predictions or generate outputs on new data. While training happens once (or periodically), inference happens continuously in production systems. Inference optimization is crucial for cost-effective AI deployment at scale.

Instruction Tuning

Fine-tuning language models specifically to follow human instructions more effectively. Instruction-tuned models are better at understanding and executing user requests, making them more practical for real-world applications.

L

Large Language Model (LLM)

Neural networks trained on massive text datasets that can understand and generate human language. LLMs like GPT-4, Claude, and Gemini demonstrate remarkable versatility across tasks—from writing and analysis to coding and reasoning. Our LLM Residency program helps organizations harness these capabilities systematically.

Learning Rate

A hyperparameter that controls how much model parameters change during training. Too high, and training becomes unstable; too low, and learning is impractically slow. Finding the right learning rate is one of many optimization decisions in AI development.

M

Machine Learning (ML)

A subset of AI where systems learn patterns from data rather than following explicitly programmed rules. Machine learning encompasses supervised learning (learning from labeled examples), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error).

Model

A mathematical representation trained on data to make predictions or generate outputs. In AI terminology, “model” refers to the entire system of algorithms and learned parameters that performs specific tasks. Models can range from simple linear regressions to complex neural networks with billions of parameters.

Multimodal AI

Systems that can process and generate multiple types of data—text, images, audio, video—and understand relationships between them. Multimodal capabilities represent the next frontier in AI, enabling richer interactions and more comprehensive understanding.

N

Natural Language Processing (NLP)

The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, translation services, sentiment analysis, text summarization, and search engines. Modern LLMs represent a quantum leap in NLP capabilities.

Neural Network

Computing systems inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers. Neural networks learn by adjusting connection strengths based on training data. They’re the foundation of deep learning and most modern AI systems.

O

Overfitting

When a model learns training data too well, including noise and irrelevant patterns, resulting in poor performance on new data. Overfitting is like memorizing exam answers without understanding concepts—you ace the practice test but fail the real one. Preventing overfitting requires techniques like regularization, data augmentation, and proper validation.

P

Parameter

Numerical values in a model that are learned from training data and determine how the model processes inputs. Modern LLMs have billions or even trillions of parameters. While parameter count correlates with capability, it’s not the only factor—architecture, training data quality, and fine-tuning matter just as much.

Prompt

The input text or instructions given to an LLM to elicit desired outputs. Effective prompt engineering—crafting prompts that consistently produce useful results—is both a skill and a science. Our teams help organizations develop prompt libraries and best practices for their specific use cases.

Prompt Engineering

The practice of designing and optimizing prompts to achieve desired outputs from LLMs. Good prompt engineering includes providing context, specifying format, giving examples, and iteratively refining based on results. It’s a core competency for practical LLM deployment.

R

Reinforcement Learning (RL)

A machine learning approach where agents learn by interacting with an environment and receiving rewards or penalties. RL has achieved superhuman performance in games and is increasingly applied to robotics, resource optimization, and autonomous systems.

Retrieval-Augmented Generation (RAG)

A technique that enhances LLM outputs by retrieving relevant information from a knowledge base before generating responses. RAG significantly reduces hallucinations and enables LLMs to work with proprietary or current data beyond their training cutoff. It’s the most practical approach for enterprise LLM deployment—we implement RAG systems in our 4-6 week residency sprints.

Regression

A machine learning task that predicts continuous numerical values rather than categories. Examples include predicting house prices, forecasting sales, or estimating delivery times. Regression is fundamental to business analytics and operational optimization.

S

Semantic Search

Search that understands the meaning and context of queries rather than just matching keywords. Semantic search uses embeddings to find conceptually similar content even when exact words don’t match. It dramatically improves information retrieval in knowledge bases and document systems.

Supervised Learning

Machine learning where models train on labeled data—inputs paired with correct outputs. The model learns to map inputs to outputs, then applies this learning to new, unseen data. Most practical business AI applications use supervised learning.

Synthetic Data

Artificially generated data created by AI rather than collected from real-world observations. Synthetic data can address privacy concerns, fill gaps in training datasets, or create edge-case scenarios for testing. However, quality validation is essential—synthetic data should augment, not replace, real-world data.

T

Temperature

A parameter controlling randomness in LLM outputs. Low temperature produces focused, deterministic responses; high temperature generates more creative, diverse outputs. Adjusting temperature is a simple but powerful way to tune LLM behavior for different use cases.

Token

The basic unit of text processing in LLMs, roughly corresponding to words or word fragments. LLMs process text by converting it to tokens, and their costs and capabilities are measured in tokens. Understanding tokenization helps optimize both performance and costs.

Training Data

The dataset used to teach an AI model during the training process. Training data quality fundamentally determines model capabilities—garbage in, garbage out remains true for AI. Systematic data curation and validation are essential for production-quality models.

Transfer Learning

Using a model trained on one task as the starting point for learning a related task. Transfer learning dramatically reduces the time and data required to develop effective models. It’s the foundation of modern AI efficiency—we don’t build from scratch when we can adapt proven capabilities.

Transformer

A neural network architecture that uses attention mechanisms to process sequential data efficiently. Transformers revolutionized AI by enabling parallel processing and better handling of long-range dependencies. They power virtually all modern LLMs and many other AI systems.

U

Unsupervised Learning

Machine learning where models find patterns in unlabeled data without explicit guidance. Common applications include clustering customers into segments, detecting anomalies, or reducing data dimensionality. Unsupervised learning helps discover hidden structures in data.

V

Vector Database

A specialized database optimized for storing and querying embeddings. Vector databases enable fast similarity search across millions of items, making them essential for RAG systems, recommendation engines, and semantic search. Popular options include Pinecone, Weaviate, and pgvector.

Z

Zero-Shot Learning

A model’s ability to perform tasks it wasn’t explicitly trained for by generalizing from instructions or examples provided in the prompt. Modern LLMs demonstrate impressive zero-shot capabilities, though performance typically improves with few-shot learning (providing a few examples).

Navigating AI Terminology in Practice

Understanding AI terminology is just the beginning. The real challenge is applying these concepts to solve actual business problems—systematically, reliably, and with measurable impact. At Far Horizons, we don’t just implement AI technology; we engineer solutions that work the first time and deliver lasting value.

From Glossary to Implementation

This AI glossary provides the vocabulary you need to:

Evaluate AI vendors with technical literacy
Communicate effectively with data science teams
Understand feasibility of proposed solutions
Identify risks in AI projects
Make informed decisions about technology investments

But knowing the terms isn’t the same as knowing which technologies solve which problems, how to validate them systematically, or how to deploy them reliably.

Your Next Steps

If you’re navigating AI adoption for your organization, you’re facing critical questions:

Which AI capabilities actually address our business challenges?
How do we validate technology fit before significant investment?
What’s the systematic path from proof-of-concept to production?
How do we build internal capabilities rather than perpetual vendor dependence?
What governance frameworks ensure responsible, compliant AI deployment?

Our LLM Residency program answers these questions through embedded 4-6 week sprints that deliver:

Production-ready RAG systems tailored to your knowledge base
Workflow automation that measurably improves productivity
Prompt engineering training that upskills your entire team
AI governance frameworks that ensure responsible deployment
Systematic methodologies you can apply long after we’re gone

We bring the same disciplined approach to AI that put humans on the moon—because you don’t get to innovation moonshots by being a cowboy.

Ready to Move from Terminology to Transformation?

Whether you’re just beginning to explore AI possibilities or you’re ready to scale existing initiatives, Far Horizons provides the systematic guidance and hands-on expertise to ensure success.

Schedule your innovation assessment to discover how systematic AI adoption can create competitive advantage for your organization.

Explore our LLM Residency program to see how we embed with your team to deliver production-ready AI solutions in weeks, not years.

Download our AI Readiness Framework for a comprehensive self-assessment of your organization’s AI maturity and opportunities.

Keeping Current with AI Terminology

AI terminology evolves as rapidly as the technology itself. New terms emerge, definitions shift, and yesterday’s cutting-edge becomes today’s standard practice. We update this AI glossary regularly to reflect the current state of the field.

For the latest insights on AI, LLMs, and systematic innovation, follow Far Horizons or reach out to discuss how we can help your organization navigate this transforming landscape.

Innovation Engineered for Impact | Far Horizons

Last updated: November 2025