Artificial Intelligence Glossary
Generative AI, LLMs, prompt engineering, AI tools, and machine learning terminology
A
AI Agent
Autonomous AI systems that can plan, execute multi-step tasks, use tools, and take actions to achieve goals.
AI Alignment
Ensuring AI systems behave according to human values, intentions, and goals rather than causing unintended harm.
AI Content Detection
Tools and techniques designed to identify whether content was generated by AI rather than written by humans.
AI Copilot
An AI assistant embedded in software to help users complete tasks more efficiently.
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 Overview
Google's AI-generated summary boxes that appear at the top of search results, synthesizing information from multiple sources.
Adversarial Examples
Carefully crafted inputs designed to fool AI models into making mistakes, often imperceptible to humans but causing system 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 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.
Attention Mechanism
AI technique that helps models focus on relevant parts of input data, enabling better understanding of context and relationships.
Attribution Modeling
AI-powered analysis that determines which marketing touchpoints contribute to conversions, enabling better budget allocation decisions.
B
C
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.
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.
Context Window
The maximum amount of text an AI model can consider at once during a conversation or task.
Conversational AI
AI systems designed to engage in natural dialogue with humans through text or voice, powering chatbots and virtual assistants.
D
Data Poisoning
Malicious manipulation of training data to compromise AI model behavior, causing models to make incorrect or biased decisions.
Diffusion Model
An AI architecture that generates images by progressively refining random noise into coherent outputs.
Dynamic Pricing
AI-driven strategy that automatically adjusts prices in real-time based on demand, competition, inventory, and market conditions.
E
Embedding
A numerical representation of text that captures its meaning for comparison and search.
Ensemble Learning
Combining multiple AI models to make predictions, typically achieving better accuracy than any single model alone.
Explainable AI
AI systems designed to provide clear explanations for their decisions, enabling humans to understand and trust automated choices.
F
Federated Learning
Training AI models across distributed devices without centralizing data, preserving privacy while enabling collaborative learning.
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.
G
Generative Engine Optimization (GEO)
Optimizing content to appear in AI-generated search results and summaries from tools like ChatGPT, Perplexity, and Google AI Overviews.
Guardrails
Safety mechanisms that prevent AI from generating harmful, inappropriate, or off-topic content.
I
L
M
Model Collapse
Degradation in AI quality when models are trained on AI-generated content.
Model Drift
Gradual degradation of AI model performance over time as real-world data patterns change from original training conditions.
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.
N
Natural Language Processing (NLP)
The field of AI focused on enabling computers to understand, interpret, and generate human language.
Neural Architecture Search
Automated process of discovering optimal neural network designs using AI to find the best model structure for specific tasks.
P
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.
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.
Q
R
RAG (Retrieval-Augmented Generation)
A technique that enhances AI responses by retrieving relevant information from external knowledge sources.
Reinforcement Learning
AI training method where models learn through trial and error, receiving rewards for good decisions and penalties for bad ones.
Retrieval-Augmented Generation (RAG)
AI technique that enhances LLM responses by retrieving relevant information from external databases before generating answers.
S
Structured Output
AI responses formatted in consistent data structures like JSON for programmatic processing.
Synthetic Data Generation
Creating artificial datasets that mimic real data patterns, used for training AI models when actual data is limited or sensitive.
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.
Transfer Learning
Leveraging pre-trained AI models and adapting them for new tasks, reducing training time and data requirements.
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