Artificial Intelligence Glossary
Generative AI, LLMs, AI tools, and machine learning terminology
A
AI API
Programming interfaces that enable applications to access AI model capabilities through simple function calls.
AI Agent
Autonomous AI systems that can plan, execute multi-step tasks, use tools, and take actions to achieve goals.
AI Agents
AI systems that can autonomously take actions and make decisions to accomplish goals.
AI Alignment
Ensuring AI systems behave according to human values, intentions, and goals rather than causing unintended harm.
AI Audit Trail
Comprehensive record of AI system decisions, data inputs, and model changes that enables accountability and compliance verification.
AI Automation
Using artificial intelligence to automate tasks, workflows, and decision-making processes that traditionally required human intervention.
AI Bias
Systematic errors in AI outputs that reflect prejudices in training data or model design.
AI Chatbot
Automated conversational interfaces that use AI to understand and respond to user questions and requests.
AI Code Generation
AI systems that automatically write, complete, or suggest code based on natural language prompts or context.
AI Coding Assistant
Software tools that use AI to help developers write, review, and debug code through real-time suggestions and automation.
AI Content Detection
Tools and techniques designed to identify whether content was generated by AI rather than written by humans.
AI Content Generation
Using artificial intelligence to create marketing content including copy, images, videos, and other creative assets.
AI Copilot
An AI assistant embedded in software to help users complete tasks more efficiently.
AI Copywriting
Using artificial intelligence to generate marketing copy, ad text, and content at scale.
AI Gateway
Centralized service managing AI API access, costs, and observability.
AI Governance
Policies and frameworks for responsible AI development, deployment, and oversight.
AI Hallucination
When AI models generate false, fabricated, or nonsensical information that appears plausible but has no basis in fact.
AI Image Generation
Using AI models to create visual content from text descriptions or other inputs.
AI Inference
Using a trained AI model to make predictions or generate outputs.
AI Jailbreak
Techniques to bypass AI safety constraints and make models produce restricted content.
AI Observability
Tools and practices for monitoring AI system performance, behavior, and outputs in production.
AI Orchestration
Coordinating multiple AI models and services to accomplish complex tasks.
AI Orchestration Layer
Software that coordinates multiple AI models and services to complete complex tasks.
AI Overview
Google's AI-generated summary boxes that appear at the top of search results, synthesizing information from multiple sources.
AI Personalization
Using machine learning to deliver individualized content, recommendations, and experiences to each user.
AI Prompt Engineering
The practice of crafting effective prompts to get optimal responses from AI language models.
AI Red Teaming
Systematically testing AI systems for vulnerabilities, biases, and harmful outputs.
AI Safety
The field focused on ensuring AI systems operate safely, reliably, and as intended.
AI Scaling Laws
Mathematical relationships between model size, data, compute, and performance.
AI Sovereignty
The principle that nations or organizations maintain control over their AI systems, data, and decision-making processes.
AI Summit
Business-focused AI conference series exploring enterprise artificial intelligence applications and strategy.
AI Watermarking
Invisible markers embedded in AI-generated content to identify its synthetic origin.
AI Workflow Automation
Using AI to automate multi-step business processes with intelligent decision-making.
AI-Powered Attribution
Using machine learning to determine the true contribution of marketing touchpoints to conversions.
Activation Function
Mathematical function in neural networks that determines whether a neuron should be activated based on input relevance to predictions.
Adaptive Learning Rate
Dynamic adjustment of model training speed based on performance feedback to optimize learning efficiency and convergence.
Adversarial Examples
Carefully crafted inputs designed to fool AI models into making mistakes, often imperceptible to humans but causing system failures.
Adversarial Robustness
An AI model's ability to maintain correct performance when facing deliberately crafted inputs designed to cause failures.
Adversarial Training
Training method where neural networks compete against each other to improve robustness and reduce vulnerabilities.
Agent
An AI system that can autonomously take actions and make decisions to accomplish goals.
Agentic AI
AI systems capable of autonomous decision-making and goal-directed behavior.
Agentic Memory
Long-term memory systems that allow AI agents to retain and recall information across sessions.
Agentic RAG
An advanced retrieval-augmented generation approach where AI agents autonomously decide what to search for and when.
Agentic Reasoning
AI capability where models autonomously plan, execute, and iterate on multi-step tasks without constant human guidance.
Agentic Workflow
A multi-step process where AI agents autonomously complete complex tasks.
Algorithmic Bias
Systematic unfairness in AI systems that discriminates against certain groups due to biased training data or flawed algorithms.
Algorithmic Transparency
Practice of making AI decision-making processes understandable and accountable to users, regulators, and affected stakeholders.
Anomaly Detection
AI techniques that identify unusual patterns or outliers in data that may indicate problems, opportunities, or fraud.
Anthropic
An AI safety company and creator of Claude, focused on building reliable and interpretable AI systems.
Attention Mechanism
AI technique that helps models focus on relevant parts of input data, enabling better understanding of context and relationships.
Attention Visualization
Visual representations of which input elements AI models focus on when making decisions, providing insight into model reasoning.
Attribution Modeling
AI-powered analysis that determines which marketing touchpoints contribute to conversions, enabling better budget allocation decisions.
B
Backpropagation
Algorithm that trains neural networks by calculating error gradients and adjusting weights backward through network layers.
Batch Processing
Method of processing multiple data samples simultaneously to improve computational efficiency during AI model training and inference.
Behavioral Clustering
AI-driven segmentation that groups customers based on observed actions and interaction patterns rather than demographic data.
Benchmark
Standardized tests used to evaluate and compare AI model performance.
C
Catastrophic Forgetting
When fine-tuning an AI model on new data causes it to lose previously learned capabilities and knowledge.
Causal Inference
AI methods that identify cause-and-effect relationships in data rather than just correlations, enabling better decision-making.
Chain of Thought
A prompting technique that improves AI reasoning by requesting step-by-step explanations.
ChatGPT
OpenAI's conversational AI assistant powered by GPT models, widely used for content creation, research, and task automation.
Chatbot Marketing
Using conversational AI to engage prospects, qualify leads, and guide customers through marketing funnels.
Churn Prediction
Machine learning models that identify customers likely to stop using your product or cancel their subscription.
Citability
How easily AI systems can identify, extract, and cite specific information from your content in their generated responses.
Claude
Anthropic's AI assistant known for nuanced understanding, strong reasoning, and safety-focused design.
Cognitive Load Reduction
Using AI to simplify decision-making processes for customers by reducing the mental effort required to evaluate options.
Cohere
An enterprise AI company specializing in language models for business applications and search.
Compound AI Systems
AI applications that combine multiple models, retrievers, and tools to solve complex tasks.
Computer Vision
A field of AI that enables computers to interpret and understand visual information from the world.
Constitutional AI
An approach to AI training that uses explicit principles to guide model behavior, making AI systems more predictable and aligned with human values.
Context Length
The maximum amount of text an AI model can process in a single conversation or request.
Context Stuffing
Including extensive background information in AI prompts to improve response quality.
Context Window
The maximum amount of text an AI model can consider at once during a conversation or task.
Contextual Embeddings
Vector representations that capture word or concept meanings based on surrounding context rather than fixed definitions.
Continual Learning
AI systems' ability to learn new tasks and information without forgetting previously acquired knowledge and skills.
Conversational AI
AI systems designed to engage in natural dialogue with humans through text or voice, powering chatbots and virtual assistants.
Cross Validation
Statistical method for assessing how well an AI model will generalize to new data by testing on multiple data subsets.
D
DALL-E
OpenAI's text-to-image AI model that generates original images from natural language descriptions.
Data Augmentation
Techniques that artificially expand training datasets by creating modified versions of existing data to improve model performance.
Data Flywheel
A self-reinforcing cycle where AI usage generates data that improves the model, which attracts more usage and more data.
Data Poisoning
Malicious manipulation of training data to compromise AI model behavior, causing models to make incorrect or biased decisions.
Data Sovereignty
Legal and ethical principle that data is subject to the laws and governance of the country or jurisdiction where it is collected.
Deep Learning
A subset of machine learning using neural networks with many layers to analyze complex patterns in data.
Diffusion Model
An AI architecture that generates images by progressively refining random noise into coherent outputs.
Dynamic Creative Optimization
AI technology that automatically tests and serves the best-performing ad creative combinations to each viewer.
Dynamic Pricing
AI-driven strategy that automatically adjusts prices in real-time based on demand, competition, inventory, and market conditions.
E
Edge Computing AI
Running AI models directly on local devices rather than cloud servers to reduce latency, improve privacy, and enable offline operation.
Eleven Labs
An AI company specializing in realistic text-to-speech and voice cloning technology.
Embedding
A numerical representation of text that captures its meaning for comparison and search.
Emergent Behavior
Unexpected capabilities that appear in AI models at scale without being explicitly programmed or trained for.
Ensemble Learning
Combining multiple AI models to make predictions, typically achieving better accuracy than any single model alone.
Evaluation Framework
Systematic approach to measuring AI system quality and performance.
Explainable AI
AI systems designed to provide clear explanations for their decisions, enabling humans to understand and trust automated choices.
F
Fairness Metrics
Quantitative measures used to evaluate whether AI systems treat different groups equitably across protected characteristics.
Feature Engineering
Process of selecting, transforming, and creating input variables that help machine learning models make better predictions.
Federated Learning
Training AI models across distributed devices without centralizing data, preserving privacy while enabling collaborative learning.
Feedback Loop Integration
Systems that continuously improve AI performance by incorporating user interactions and outcomes back into model training.
Few-Shot Learning
Teaching AI new tasks by providing just a few examples in the prompt.
Fine-Tuning
Training an existing AI model on specialized data to improve performance for specific tasks or domains.
Foundation Model
Large AI models trained on broad data that serve as the base for many applications, like GPT-4 or Gemini.
Frontier Model
The most advanced AI models representing the current cutting edge of capabilities.
G
GPT (Generative Pre-trained Transformer)
A family of large language models that generate human-like text based on input prompts.
GPT-4
OpenAI's most capable large language model, powering ChatGPT Plus and enterprise AI applications.
GPT-4o
OpenAI's optimized multimodal model offering GPT-4 intelligence at faster speeds and lower costs.
Gemini
Google's family of multimodal AI models powering Bard, Search, and Google Cloud AI services.
Generative AI Marketing
Applying generative AI models to create marketing content, visuals, and campaigns at scale.
Generative Engine Optimization (GEO)
Optimizing content to appear in AI-generated search results and summaries from tools like ChatGPT, Perplexity, and Google AI Overviews.
GitHub Copilot
An AI pair programmer that suggests code completions and entire functions as developers type.
Gradient Descent
Optimization algorithm that iteratively adjusts model parameters by moving in the direction that minimizes prediction errors.
Groq
An AI inference company known for extremely fast LLM processing through custom hardware.
Grounding
Connecting AI model outputs to verified external information sources to improve accuracy and reduce hallucination.
Guardrail System
Safety mechanisms that constrain AI outputs to acceptable boundaries.
Guardrails
Safety mechanisms that prevent AI from generating harmful, inappropriate, or off-topic content.
H
Hugging Face
The platform hosting open-source AI models, datasets, and tools—often called the GitHub of machine learning.
Hyperparameter Optimization
Automated techniques for finding optimal configuration settings that control AI model training and performance characteristics.
I
In-Context Learning
The ability of AI models to learn new tasks from examples provided directly in the prompt without updating model weights.
Inference
The process of running a trained AI model to generate predictions or outputs from new inputs.
Inference Cost
The computational expense of running a trained AI model to generate outputs.
Inference Optimization
Techniques to make AI model predictions faster and more cost-effective.
Intent Recognition
AI's ability to understand and classify user intentions behind queries or interactions for appropriate response generation.
K
L
LangChain
A framework for building applications powered by language models with data connections and reasoning chains.
Large Language Model (LLM)
AI systems trained on massive text datasets to understand and generate human-like text, powering tools like ChatGPT and Claude.
Latency
The delay between sending a request to an AI system and receiving the response.
Latent Space
A compressed mathematical representation where AI models encode input data as points in multi-dimensional space.
Learning Rate
Hyperparameter controlling how quickly an AI model updates its parameters during training to minimize errors.
Llama
Meta's open-source large language model family, enabling anyone to run powerful AI locally or in production.
Lookalike Modeling
Machine learning technique that identifies prospects who share characteristics with existing high-value customers for targeted marketing.
Low-Rank Adaptation (LoRA)
An efficient fine-tuning method that adds small trainable layers to a frozen base model instead of updating all parameters.
M
MIT Technology Review
Publication covering emerging technologies with particular depth in AI, biotechnology, and computing.
Marketing Automation AI
AI-enhanced marketing automation that optimizes timing, content, and audience targeting automatically.
Marketing Mix Modeling
Statistical analysis that quantifies the impact of various marketing activities on sales to optimize budget allocation across channels.
Microsoft Copilot
Microsoft's AI assistant integrated across Windows, Office 365, and development tools.
Midjourney
An independent AI art generator known for distinctive aesthetic quality and artistic style.
Mistral
A French AI company known for efficient open-source models that punch above their weight class.
Mixture of Experts
An AI architecture where multiple specialized neural networks handle different types of tasks, improving efficiency and performance.
Model Card
Documentation describing an AI model capabilities, limitations, and appropriate uses.
Model Collapse
Degradation in AI quality when models are trained on AI-generated content.
Model Compression
Techniques to reduce AI model size and computational requirements while preserving performance for deployment efficiency.
Model Context Protocol (MCP)
An open standard for connecting AI assistants to external data sources, tools, and services through a unified interface.
Model Distillation
Transferring knowledge from a large AI model to a smaller, more efficient one.
Model Drift
Gradual degradation of AI model performance over time as real-world data patterns change from original training conditions.
Model Ensemble
Technique combining predictions from multiple AI models to achieve better accuracy and reliability than any single model alone.
Model Fine-Tuning
The process of training an existing AI model on specialized data to improve performance for specific tasks.
Model Garden
A curated collection of pre-trained AI models available through a single platform.
Model Governance
Framework of policies, processes, and controls that ensure responsible development, deployment, and management of AI systems.
Model Interpretability
The ability to understand and explain how AI models make decisions, crucial for trust, compliance, and debugging.
Model Merging
Combining the weights of multiple fine-tuned AI models into a single model that inherits capabilities from all of them.
Model Orchestration
The coordination of multiple AI models working together to solve complex tasks through automated workflow management.
Model Quantization
Reducing AI model size and computational requirements by using lower precision.
Model Serving
Infrastructure for deploying AI models to handle production inference requests.
Model Versioning
Systems for tracking, managing, and deploying different versions of AI models throughout development and production lifecycles.
Model Weights
The learned parameters that determine how a neural network processes information.
Multi-Task Learning
Training AI models to perform multiple related tasks simultaneously, improving efficiency and knowledge transfer between domains.
Multimodal
AI systems that can process and generate multiple types of content—text, images, audio, and video.
Multimodal AI
AI systems that can understand and generate multiple types of content including text, images, audio, and video.
Multimodal Embedding
Vector representations that capture meaning across text, images, and other modalities.
Multimodal RAG
Retrieval-augmented generation that combines text, images, and other media types.
N
Natural Language Processing (NLP)
The field of AI focused on enabling computers to understand, interpret, and generate human language.
NeurIPS (Neural Information Processing Systems)
Premier academic conference on machine learning and computational neuroscience research.
Neural Architecture Search
Automated process of discovering optimal neural network designs using AI to find the best model structure for specific tasks.
Neural Network
A computing system inspired by biological neural networks that learns patterns from data.
Neural Style Transfer
AI technique that applies the artistic style of one image to the content of another while preserving the original structure.
O
Ollama
A tool for easily running open-source LLMs locally on your own computer.
OpenAI
The AI research company behind ChatGPT, GPT-4, DALL-E, and the models powering much of the AI industry.
Overfitting
Modeling error where AI systems memorize training data too closely, performing poorly on new, unseen data.
P
Perplexity
An AI-powered search engine that provides direct answers with cited sources instead of link lists.
Perplexity AI
AI-powered answer engine that provides sourced responses to queries, representing the future of AI search.
Personalization Engine
AI system that customizes content, products, or experiences for individual users based on their preferences and behavior patterns.
Predictive Analytics
Using AI to analyze historical data and predict future trends, customer behavior, or business outcomes for strategic decision-making.
Preference Learning
AI techniques that learn individual user preferences from behavior patterns to enable personalized experiences and recommendations.
Programmatic Creative
AI-driven automatic generation and optimization of advertising creative elements based on audience data and performance metrics.
Prompt Caching
Storing and reusing processed portions of AI prompts to reduce latency and costs.
Prompt Chaining
A technique where multiple AI prompts are linked sequentially, with each output feeding into the next prompt as input.
Prompt Engineering
The practice of crafting effective instructions and queries to get optimal outputs from AI language models.
Prompt Injection
Security attack where malicious instructions are embedded in user inputs to manipulate AI language model behavior inappropriately.
Prompt Template
Reusable prompt structure with variable placeholders for consistent AI interactions.
Q
R
RAG (Retrieval-Augmented Generation)
A technique that enhances AI responses by retrieving relevant information from external knowledge sources.
RLHF
Reinforcement Learning from Human Feedback—training AI using human preferences.
Reasoning Models
AI models designed to break down complex problems into logical steps before answering.
Reasoning Trace
The visible step-by-step thinking process an AI model shows when working through complex problems before providing an answer.
Reinforcement Learning
AI training method where models learn through trial and error, receiving rewards for good decisions and penalties for bad ones.
Responsible AI
The practice of developing and deploying AI systems that are ethical, transparent, and accountable.
Retrieval Augmented Generation (RAG)
An AI architecture that retrieves relevant documents before generating responses, combining search with generation.
Retrieval-Augmented Generation (RAG)
AI technique that enhances LLM responses by retrieving relevant information from external databases before generating answers.
Runway
An AI creative tools company known for video generation and editing with models like Gen-2.
S
Semantic Search
Search that understands meaning and intent rather than just matching keywords.
Slop
Low-quality, generic AI-generated content that lacks originality, insight, or genuine value to readers.
Sora
OpenAI's text-to-video AI model capable of generating realistic, minute-long videos from text prompts.
Sparse Attention
An efficiency technique where AI models selectively attend to relevant parts of the input rather than processing every element.
Stable Diffusion
An open-source image generation model that anyone can run locally or modify for custom applications.
Structured Output
AI responses formatted in consistent data structures like JSON for programmatic processing.
Synthetic Data
Artificially generated data that mimics real-world data for AI training and testing.
Synthetic Data Generation
Creating artificial datasets that mimic real data patterns, used for training AI models when actual data is limited or sensitive.
Synthetic Media
AI-generated content including images, video, audio, and text that mimics human creation.
Synthetic Training Data
Artificially generated data used to train or fine-tune AI models.
System Prompt
Instructions given to an AI model that define its persona, capabilities, and behavioral guidelines.
T
Temperature
A setting that controls the randomness and creativity of AI-generated outputs.
Token
The basic unit of text that AI language models process, typically representing about 4 characters or 0.75 words.
Token Limit
Maximum number of tokens (text units) an AI model can process in a single request.
Tokenizer
The component that converts text into numerical tokens that AI models can process and generate.
Tool Use
AI models capability to interact with external tools, APIs, and systems to complete tasks.
Top-K Sampling
A text generation technique that limits token selection to the K most probable next tokens, balancing creativity and coherence.
Transfer Learning
Leveraging pre-trained AI models and adapting them for new tasks, reducing training time and data requirements.
V
Vector Database
Specialized databases that store and search high-dimensional vectors, enabling semantic search and AI applications.
Vibe Coding
A development approach where programmers describe what they want in natural language and AI generates the corresponding code.
Voice Search Optimization
Optimizing content and SEO strategies for voice-activated search queries via smart speakers and assistants.
W
Z
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