AGI (Artificial General Intelligence)
A hypothetical AI system that can perform most intellectual tasks at a human level across many domains.
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The AI Glossary
Essential technical terminology explained at the highest level of clarity. Designed for researchers, students, and human-centered education.
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AI Agent
A software system that can observe, reason, and take actions to achieve a goal, often using tools and memory.
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AI Alignment
The work of making AI systems behave according to human intentions, values, and safety constraints.
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AI Governance
Policies, standards, and oversight mechanisms that guide how AI is developed and used in society.
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AI Safety
A field focused on reducing harmful behavior, failures, and misuse risks in AI systems.
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Algorithm
A defined set of rules or steps that a computer follows to solve a problem or complete a task.
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Algorithmic Bias
Systematic unfairness in model outputs caused by skewed data, assumptions, or modeling choices.
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Algorithmic Transparency
How clearly an AI system's logic, data sources, and limitations are documented and understandable.
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Annotation
Human-added labels or metadata used to train or evaluate machine learning models.
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API (Application Programming Interface)
A structured way for one software system to send requests to and receive responses from another system.
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Artificial Intelligence (AI)
The broad field of building systems that perform tasks requiring pattern recognition, reasoning, language, or decision-making.
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Attention Mechanism
A model component that dynamically focuses on relevant parts of an input when producing an output.
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Autonomous System
A system that can make decisions and act with limited or no direct human control in real time.
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Backpropagation
The core training algorithm that updates model weights by propagating prediction errors backward through the network.
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Baseline Model
A simple reference model used to compare whether more complex approaches actually improve results.
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Benchmark
A standardized test or dataset used to measure and compare model performance.
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Bias
A consistent pattern of error or unfairness in data or model behavior.
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Big Data
Very large and complex datasets that require scalable storage and processing techniques.
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Black-Box Model
A model whose internal reasoning is difficult to interpret directly by humans.
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Calibration
How well a model's confidence scores match actual correctness probabilities.
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Chain-of-Thought
A reasoning style where an AI model decomposes a problem into intermediate steps.
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Classification
A task where a model assigns an input to one or more predefined categories.
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Classifier
A model designed specifically for classification tasks.
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CLIP
A multimodal model architecture that learns shared representations between text and images.
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Compute
The processing resources required to train and run models, often measured in FLOPS or GPU hours.
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Computer Vision
The branch of AI that extracts meaning from images and video.
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Context Window
The maximum amount of input tokens a language model can process at once.
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Continual Learning
Training approaches that let a model keep learning from new data without forgetting prior knowledge.
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Convolutional Neural Network (CNN)
A neural architecture optimized for processing grid-like data such as images.
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Cross-Entropy Loss
A common objective function used to train classification models by penalizing incorrect probabilities.
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Data Augmentation
Techniques that create modified training examples to improve model generalization.
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Data Drift
A shift in real-world input data over time that can degrade model performance.
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Data Labeling
The process of assigning tags or target outputs to raw data for supervised learning.
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Dataset
A collection of structured or unstructured examples used for training, validation, or testing.
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Decision Boundary
The surface in feature space that separates classes predicted by a classifier.
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Decision Tree
A model that makes predictions through a sequence of if-then feature splits.
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Deep Learning
A subset of machine learning that uses many-layer neural networks for representation learning.
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Diffusion Model
A generative architecture that learns to reverse noise to synthesize images, audio, or other content.
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Distillation
Compressing knowledge from a large teacher model into a smaller student model.
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Domain Adaptation
Methods that transfer a model trained in one domain to perform better in another domain.
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Embedding
A numeric vector representation that captures semantic meaning of text, images, or other data.
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Encoder
The component of a model that transforms input into latent representations.
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Ensemble
Combining predictions from multiple models to improve robustness or accuracy.
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Evaluation Set
A held-out dataset used to measure model quality after training.
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Explainability
The degree to which a model's behavior can be interpreted and explained to humans.
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False Negative
An incorrect prediction where a model misses a true positive case.
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False Positive
An incorrect prediction where a model incorrectly flags a negative case as positive.
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Feature
An input variable used by a model to make predictions.
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Feature Engineering
Designing or transforming input variables to make learning easier and more effective.
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Feature Extraction
Converting raw data into informative features that a model can use.
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Few-Shot Learning
Learning or adapting behavior from only a small number of examples.
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Fine-Tuning
Continuing training on domain-specific data to adapt a pre-trained model to a specific task.
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Foundation Model
A large pre-trained model that can be adapted to many downstream tasks.
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Function Calling
A model capability to generate structured calls that trigger external tools or APIs.
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GAN (Generative Adversarial Network)
A generative setup where a generator and discriminator train against each other.
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Generalization
How well a model performs on new, unseen data outside the training set.
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Generative AI
AI systems that produce new content such as text, images, audio, video, or code.
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Gradient
A vector showing how much each parameter should change to reduce loss.
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Gradient Descent
An optimization method that updates parameters in the direction that reduces error.
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Ground Truth
Trusted reference labels used to train or evaluate model outputs.
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Guardrails
Rules, checks, and controls that limit unsafe or undesired model behavior.
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Hallucination
When a model generates fluent but false or unsupported information.
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Human-in-the-Loop
A workflow where humans review, guide, or override AI outputs.
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Hyperparameter
A configuration value set before training, such as learning rate, batch size, or depth.
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In-Context Learning
A model's ability to follow patterns from examples provided directly in the prompt.
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Inference
The runtime phase where a trained model generates predictions or outputs.
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Inference-Time Compute
The amount of processing power consumed while producing each response.
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Instruction Tuning
Fine-tuning a model on instruction-response pairs to improve task following.
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Intent Classification
Predicting the user's purpose from a text query to route it correctly.
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Jailbreak
A prompt technique intended to bypass a model's safety constraints.
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Knowledge Cutoff
The latest point in time reflected in a model's training data.
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Knowledge Distillation
Training a smaller model to imitate the outputs of a larger model.
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Knowledge Graph
A graph structure of entities and relationships used for reasoning or retrieval.
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Label Smoothing
A regularization method that softens hard labels to improve generalization.
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Latency
The time between sending a request and receiving the model's output.
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Large Language Model (LLM)
A language model trained on massive text corpora to generate and analyze text.
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Learning Rate
A training hyperparameter controlling how much parameters change each update step.
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LoRA (Low-Rank Adaptation)
A parameter-efficient fine-tuning method that adds low-rank adapter matrices.
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Loss Function
A mathematical objective that quantifies prediction error during training.
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Machine Learning (ML)
Methods that allow systems to learn patterns from data and improve over time.
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Memory (Agent Memory)
Stored context an AI agent uses across steps or sessions to improve continuity.
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Mixture of Experts (MoE)
An architecture with specialized subnetworks where only selected experts run per input.
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Model Card
Documentation describing a model's intended use, metrics, limitations, and risks.
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Model Drift
Performance degradation over time as real-world conditions diverge from training assumptions.
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Model Quantization
Reducing numeric precision of model weights to decrease memory and inference cost.
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Multimodal Model
A model that can process or generate multiple data types such as text, image, and audio.
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Named Entity Recognition (NER)
An NLP task that identifies entities such as people, places, dates, or organizations.
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Natural Language Processing (NLP)
The branch of AI focused on understanding and generating human language.
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Neural Network
A layered computational model inspired by biological neurons and synapses.
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Normalization
Transforming values to a consistent scale to improve optimization stability.
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OCR (Optical Character Recognition)
Technology that converts text in images or scans into machine-readable text.
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Open-Source Model
A model released with public weights or code for inspection, adaptation, and reuse.
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Overfitting
When a model memorizes training data and performs poorly on unseen inputs.
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Parameter
A learned weight inside a model that influences its outputs.
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Parameter-Efficient Fine-Tuning (PEFT)
Methods that adapt models by training a small subset of added parameters.
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Perplexity
A language-model metric measuring how surprised the model is by true next tokens.
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Pipeline
An ordered workflow of preprocessing, model steps, and postprocessing stages.
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Precision
The proportion of predicted positives that are actually correct.
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Pretraining
Initial large-scale model training on broad data before downstream adaptation.
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Prompt
The input instructions and context provided to a generative model.
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Prompt Engineering
Designing prompts to improve output quality, reliability, and controllability.
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Prompt Injection
An attack pattern where malicious instructions are inserted into model inputs or retrieved content.
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Pruning
Removing less important model weights or neurons to reduce size and compute.
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Quantization
Converting model weights to lower precision formats such as 8-bit or 4-bit.
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RAG (Retrieval-Augmented Generation)
A method that retrieves external knowledge and feeds it into generation at inference time.
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Recall
The proportion of actual positives that a model correctly identifies.
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Recommendation System
A model pipeline that predicts user preferences for ranking content or products.
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Red Teaming
Stress-testing an AI system with adversarial prompts to reveal failures and risks.
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Reinforcement Learning
Training by reward signals where an agent learns actions that maximize long-term return.
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Reinforcement Learning from Human Feedback (RLHF)
A training method that uses human preference signals to shape model behavior.
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Retrieval
Finding relevant documents or records from a knowledge source for a query.
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Reward Model
A model that scores outputs based on preference signals, often used in RLHF pipelines.
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Robustness
A model's ability to maintain performance under noise, shifts, or adversarial inputs.
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Safety Filter
A moderation layer that blocks or rewrites unsafe model inputs or outputs.
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Scaling Law
An empirical relationship showing how performance improves with model size, data, or compute.
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Semantic Search
Search that matches meaning rather than exact keyword overlap, often using embeddings.
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Self-Supervised Learning
Learning representations from unlabeled data by predicting masked or transformed parts.
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Sentiment Analysis
An NLP task that classifies emotional tone or opinion in text.
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Small Language Model (SLM)
A compact language model optimized for lower latency, cost, or on-device usage.
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Sparse Model
A model where many parameters are zero or inactive to reduce computation.
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Supervised Learning
Training a model with labeled examples that map inputs to known outputs.
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Synthetic Data
Artificially generated data used to augment, simulate, or protect sensitive training data.
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System Prompt
A high-priority instruction that sets behavior, policy, and response style for a model.
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Temperature
A sampling setting controlling randomness in generated outputs.
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Token
A chunk of text processed by language models, such as a word piece or symbol.
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Tokenization
The process of splitting text into tokens for model input.
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Tool Use
A model's ability to call external tools such as search, calculators, or APIs.
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Top-k Sampling
A decoding strategy that samples only from the k most likely next tokens.
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Top-p (Nucleus) Sampling
A decoding strategy that samples from the smallest token set whose probabilities sum to p.
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Transfer Learning
Applying knowledge learned in one task or domain to improve another task.
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Transformer
A neural architecture that uses attention to model relationships across sequences in parallel.
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Training Loss
The model error value computed during training and optimized downward over time.
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Unsupervised Learning
Learning patterns from unlabeled data without explicit target outputs.
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Validation Set
A dataset used during development to tune models and prevent overfitting.
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Vector Database
A database optimized for storing and querying high-dimensional embedding vectors.
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Vision-Language Model (VLM)
A multimodal model that jointly processes visual and textual information.
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Weak Supervision
Using noisy, heuristic, or partial labels to train models when clean labels are scarce.
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Weight
A learned numeric value that scales signals passing through a neural network.
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Word Embedding
A dense vector representation of words capturing semantic relationships.
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XAI (Explainable AI)
Techniques and practices for making AI predictions more transparent and understandable.
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Zero-Shot Learning
Solving tasks without task-specific examples by relying on prior general knowledge.
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Agentic Workflow
A multi-step process where an AI system plans, executes, checks results, and iterates toward a goal.
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AI Act
The European Union's risk-based regulatory framework for AI systems and providers.
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Alignment Tax
The extra cost in time, compute, or product velocity required to make systems safer and more controllable.
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Benchmark Leakage
When benchmark test examples or close variants are present in training data, inflating reported performance.
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Causal Inference
Methods for estimating cause-and-effect relationships rather than simple correlations.
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Confidence Interval
A statistical range that likely contains the true value of a measured model metric.
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Constitutional AI
A training and behavior-shaping approach where model outputs are guided by a fixed set of written principles.
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Data Lineage
A record of where data came from, how it was transformed, and where it is used.
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Data Provenance
The documented origin, ownership, and history of a dataset or model artifact.
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Differential Privacy
A privacy technique that adds statistical noise so individual records cannot be reliably inferred from outputs.
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Distilled Model
A smaller model trained to imitate a larger model's behavior while using less compute at inference.
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Embedding Model
A model specialized for converting data into vectors used for semantic search, clustering, and retrieval.
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Eval Harness
A repeatable evaluation framework that runs prompts, datasets, and scoring logic across model versions.
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Feature Store
A managed system for storing and serving validated ML features consistently for training and inference.
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Groundedness
The degree to which an AI response is supported by source data or retrieved evidence.
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Guarded Decoding
A generation strategy that constrains output tokens to valid structures or policy-compliant choices.
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Human Preference Model
A model trained on human rankings to predict which responses users are likely to prefer.
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Inference Endpoint
A deployed API interface that receives model requests and returns predictions in production.
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Knowledge Base
A curated collection of documents or records used for retrieval, support automation, or grounding responses.
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Latent Space
A compressed representational space where similar concepts are positioned near each other as vectors.
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Model Registry
A central catalog for versioning, approving, and tracking models across environments.
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On-Device AI
AI inference performed locally on user hardware rather than in a remote cloud service.
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Output Parser
Logic that validates and converts model output into strongly typed, machine-usable structures.
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Prompt Template
A reusable prompt pattern with variables, formatting rules, and task-specific instructions.
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Retrieval Precision
The proportion of retrieved items that are relevant to the user's query.
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Safety Case
A structured argument, supported by evidence, that an AI system is safe for a defined context of use.
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Shadow Deployment
Running a model in parallel with production traffic without affecting user-facing decisions.
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Structured Output
Model output constrained to a defined schema such as JSON, tool arguments, or typed fields.
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Test-Time Compute
Additional inference computation used during response generation to improve quality or reasoning.
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Trust Calibration
Aligning user confidence in AI outputs with the system's actual reliability in each task.
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Usage-Based Billing
Pricing where costs scale with API calls, tokens, inference time, or consumed compute.
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Zero Data Retention
A policy where request/response payloads are not stored after processing beyond short-lived operational windows.
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