Activation Function
A mathematical function applied to neuron outputs that introduces non-linearity, enabling neural networks to learn complex patterns.
AI Glossary
Quick, practical definitions for common AI terms.
Adversarial Examples
Inputs deliberately designed with small perturbations to cause machine learning models to make incorrect predictions.
Agent
A system that uses an LLM plus tools, memory, and logic to execute multi-step tasks.
Agent Framework
A software environment used to build AI agents that combine LLMs with tools, memory, planning, and reasoning to complete complex tasks.
Agentic AI
AI systems designed to autonomously plan, make decisions, and execute multi-step workflows with minimal human intervention.
AGI
Artificial General Intelligence. A theoretical form of AI that possesses the ability to understand, learn, and apply knowledge across any intellectual task a human can do.
AI Alignment Problem
The challenge of ensuring advanced AI systems pursue goals that are fully aligned with human values and intentions.
AI Memory
A system that allows AI models or agents to store and retrieve past information or interactions to improve future responses.
AI Safety
The field focused on ensuring AI systems behave reliably, ethically, and without causing harm to individuals or society.
Alignment
The process of ensuring an AI's goals and behaviors are consistent with human values, ethics, and intended safety protocols.
API
Application Programming Interface. A defined way for software systems to communicate, often used to call AI models or services.
Attention Mechanism
A model technique that helps focus on the most relevant parts of input when producing each output token.
AutoML
Automated Machine Learning. Tools and techniques that automate the process of applying machine learning to real-world problems, including model selection and hyperparameter tuning.
Backpropagation
An algorithm used to train neural networks by calculating gradients of the loss function and updating weights to minimize prediction errors.
Baseline Model
A simple, reference model used as a benchmark to compare the performance of more sophisticated models.
Batch Size
The number of training examples processed together before updating model parameters during training.
Benchmark
A standardized dataset or task used to compare the performance of different AI models.
BERT
Bidirectional Encoder Representations from Transformers. A pre-trained language model that reads text bidirectionally to better understand context.
Bias
Systematic skew in model outputs that can result from imbalanced data, training objectives, or deployment context.
Chain-of-Thought
A prompting technique that encourages models to show their reasoning process step-by-step, often improving complex problem-solving.
Classification
A supervised learning task where the model predicts which category or class an input belongs to from a predefined set.
Clustering
An unsupervised learning technique that groups similar data points together based on their characteristics or features.
CNN
Convolutional Neural Network. A deep learning architecture specialized for processing grid-like data such as images, using convolutional layers to detect features.
Computer Vision
A field of AI that enables computers to derive meaningful information from digital images, videos, and other visual inputs.
Confusion Matrix
A table used to evaluate classification model performance by showing true positives, false positives, true negatives, and false negatives.
Context Window
The maximum amount of text (in tokens) a model can consider in a single request.
Cross-Validation
A technique for assessing model performance by training and evaluating on different subsets of data to ensure robustness and generalization.
Data Augmentation
Techniques that create modified versions of training data to increase dataset size and diversity, improving model robustness.
Dataset
A structured collection of examples used to train, validate, or test machine learning models.
Deep Learning
A subset of machine learning using neural networks with multiple hidden layers to learn hierarchical representations of data.
Diffusion Model
A type of generative model, often used for image creation, that learns to generate data by reversing a process of adding noise to images.
Dropout
A regularization technique that randomly deactivates neurons during training to prevent overfitting and improve model generalization.
Edge AI
Running AI models directly on local devices (edge devices) rather than in the cloud, enabling faster responses and better privacy.
Embedding
A numeric vector representation of text, image, or other data that captures semantic meaning.
Ensemble Learning
A method that combines predictions from multiple models to achieve better performance than any single model alone.
Epoch
One complete pass through the entire training dataset during the model training process.
Explainable AI (XAI)
A set of processes and methods that allow human users to comprehend and trust the results and output created by machine learning algorithms.
Feature Engineering
The process of selecting, transforming, and creating input variables (features) from raw data to improve model performance.
Federated Learning
A distributed machine learning approach where models are trained across multiple devices or servers without centralizing sensitive data.
Few-Shot Prompting
Providing a small number of examples in a prompt so a model can infer the intended pattern or format.
Fine-Tuning
Further training a base model on a specific dataset so it performs better on targeted tasks.
Foundation Model
A large pre-trained model trained on vast datasets that can be adapted to many downstream tasks such as text generation, coding, or image creation.
Function Calling
A mechanism where language models generate structured outputs that trigger predefined functions or API calls in software applications.
GAN
Generative Adversarial Network. A framework where two neural networks (generator and discriminator) compete, with the generator creating synthetic data and the discriminator evaluating its authenticity.
Generative AI
AI systems that can create new content such as text, images, audio, or code based on patterns learned from training data.
GPT
Generative Pre-trained Transformer. A family of autoregressive language models that generate text by predicting one token at a time.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters in the direction that reduces the loss function.
Guardrails
Rules, checks, and constraints applied to AI systems to reduce unsafe, incorrect, or policy-violating outputs.
Hallucination
When a model returns information that sounds plausible but is incorrect or unsupported.
Hallucination Mitigation
Techniques used to reduce incorrect or fabricated outputs from AI models, often using retrieval systems, guardrails, or verification methods.
Hyperparameters
Configuration values set before training or inference, such as learning rate, batch size, or generation temperature.
Inference
The step where a trained model produces predictions or generated outputs for new input.
Inference Time
The duration required for a trained model to process input data and generate predictions or outputs.
Jailbreaking
Attempts to bypass an AI system's safety restrictions by crafting prompts that trick the model into ignoring safeguards.
Knowledge Distillation
A technique where a smaller 'student' model learns to mimic the behavior of a larger, more complex 'teacher' model.
Latency
The response delay between sending a request and receiving a result.
Latency Optimization
Techniques used to reduce AI response time, including caching, batching, model compression, and edge deployment.
Latent Space
A compressed representation of data learned by a model where similar inputs are positioned close together in a high-dimensional space.
LLM
Large Language Model. A neural network trained on massive text data to understand and generate language.
LLMOps
Operational practices and tools used to deploy, monitor, evaluate, and maintain large language model applications in production.
Loss Function
A mathematical function that measures how far a model's predictions are from the actual target values, used to guide training.
Machine Learning
A field of AI focused on developing algorithms that enable computers to learn patterns and make decisions from data without explicit programming.
MCP
Model Context Protocol. A standard for connecting AI models to tools, data sources, and external systems.
Model Architecture
The structural design and organization of a neural network, including the number and types of layers, connections, and computational operations.
Model Compression
Techniques like pruning, quantization, and distillation used to reduce model size and computational requirements while maintaining performance.
Model Drift
A decline in model performance over time due to changes in real-world data patterns or behavior.
Model Evaluation
The process of assessing AI model performance using metrics, benchmarks, or human review to ensure accuracy, reliability, and safety.
Model Interpretability
The degree to which a human can understand and explain the decisions made by a machine learning model.
Multimodal AI
An AI system that can process and relate information from different types of data, such as combining text, images, and audio.
Neural Network
A computing system inspired by biological neural networks that learns patterns from data through interconnected nodes (neurons) organized in layers.
NLP
Natural Language Processing. A branch of AI focused on enabling computers to understand, interpret, and generate human language.
OCR
Optical Character Recognition. The use of AI to convert images of typed, handwritten, or printed text into machine-encoded text.
Overfitting
When a model learns training data too well, including noise and outliers, resulting in poor performance on new, unseen data.
Parameters
The internal variables or 'weights' learned by a model during training that determine how it transforms input data into output.
Perplexity
A metric used to evaluate language models, measuring how well the model predicts text. Lower perplexity indicates better performance.
Pre-training
The initial training phase where a model learns general patterns from large amounts of data before being specialized for specific tasks.
Prompt
The input instruction or context you provide to a model to guide its output.
Prompt Engineering
The practice of designing and refining prompts to guide AI models toward producing accurate, useful, and structured outputs.
Prompt Injection
A security attack where malicious instructions are embedded in inputs to manipulate or override an AI system's intended behavior.
Prompt Template
A reusable structured prompt format with placeholders used to generate consistent instructions for AI models.
Quantization
A technique that reduces model size and memory usage by storing weights with lower numerical precision.
RAG
Retrieval-Augmented Generation. A technique that enhances LLM responses by retrieving relevant information from an external knowledge base before generating text.
Regression
A supervised learning task where the model predicts a continuous numerical value based on input features.
Regularization
Techniques applied during training to prevent overfitting by adding constraints or penalties to the model's complexity.
Reinforcement Learning
A machine learning approach where an agent learns optimal behaviors through trial and error, receiving rewards or penalties based on its actions.
Retrieval System
A system that searches external knowledge sources and returns relevant information to an AI model before generating responses.
RLHF
Reinforcement Learning from Human Feedback. A method of training AI where human rankings of model outputs are used to optimize its performance.
RNN
Recurrent Neural Network. A neural network architecture designed for sequential data, where connections form cycles to maintain information about previous inputs.
Semantic Search
A search technique that understands the meaning and context of queries to find relevant results beyond simple keyword matching.
Structured Output
AI responses formatted in a predictable schema such as JSON, XML, or tables so they can be easily processed by software systems.
Supervised Learning
A machine learning method where models learn from labeled training data, with inputs paired to known correct outputs.
Synthetic Data
Artificially generated data that mimics real-world data, used for training when real data is scarce, expensive, or sensitive.
System Prompt
An initial instruction that sets the behavior, role, and constraints for an AI assistant throughout a conversation.
Temperature
A generation setting controlling randomness: lower values are more deterministic, while higher values produce more varied outputs.
Token
A chunk of text used by language models for processing and billing. It can be a word, subword, or symbol.
Tokenization
The process of breaking down text into smaller units (tokens) such as words, subwords, or characters for model processing.
Tool Calling
A capability that allows an AI model to invoke external functions, APIs, or software tools during inference to perform actions or retrieve information.
Transfer Learning
Reusing a pre-trained model's learned features as a starting point for a related but different task.
Transformer
A neural network architecture that uses attention mechanisms to process sequential data in parallel, forming the foundation of most modern LLMs.
Underfitting
When a model is too simple to capture the underlying patterns in data, resulting in poor performance on both training and test data.
Unsupervised Learning
A machine learning approach where models find patterns and structures in unlabeled data without predefined categories or outcomes.
Vector Database
A specialized database that stores data as numerical vectors, allowing AI models to perform fast similarity searches for relevant context.
Zero-Shot Prompting
Asking a model to perform a task without examples, relying only on instructions and its prior training.