AWS AI Practitioner key terms

The broad field of building computer systems that perform tasks normally requiring human intelligence, such as reasoning, language understanding, perception, and decision-making. It encompasses machine learning, generative AI, and rule-based approaches.

A subset of AI where algorithms learn patterns from data rather than following hand-written rules, then use those patterns to make predictions or decisions on new, unseen data.

A training approach that uses labeled examples, where each input is paired with the correct answer, so the model learns to map inputs to outputs. Common for classification and regression tasks.

A training approach that works with unlabeled data, letting the algorithm discover structure such as clusters or groupings on its own. Used for segmentation, anomaly detection, and pattern discovery.

An unsupervised technique that groups similar data points together based on shared characteristics, without predefined labels. Useful for customer segmentation, document grouping, and exploratory analysis.

A training paradigm where an agent learns by taking actions in an environment and receiving rewards or penalties, gradually discovering behavior that maximizes cumulative reward. Often used for control and game-playing tasks.

The dataset used to teach a model its patterns and parameters. Its quality, size, and representativeness strongly influence model accuracy and fairness.

The process of using a trained model to produce predictions or outputs on new input data. It happens after training and is what end users typically trigger in production.

The practice of measuring how well a trained model performs on data it has not seen, using metrics and held-out test sets, to judge accuracy, generalization, and readiness for deployment.

When a model learns its training data too closely, including noise, and performs poorly on new data. It signals weak generalization and is often addressed with more data or regularization.

A category of AI that creates new content such as text, images, audio, or code in response to a prompt, rather than only classifying or predicting labels from existing data.

A large model pretrained on broad data that can be adapted to many downstream tasks through prompting or fine-tuning, instead of being built for a single purpose.

A foundation model trained on vast amounts of text to understand and generate language. It predicts likely token sequences and powers tasks like summarization, translation, and question answering.

A small unit of text, such as a word, word fragment, or character, that a language model processes. Token counts drive context limits and often pricing.

The input instruction or question given to a generative model to guide its output. Clear, specific prompts generally produce more relevant and reliable responses.

The practice of designing and refining prompts, including instructions, examples, and formatting, to obtain accurate, relevant, and well-structured responses from a foundation model.

A prompting technique that includes a small number of worked examples in the prompt so the model can infer the desired format or reasoning pattern before answering the real request.

Asking a model to perform a task with instructions only and no worked examples. It relies on the model's pretrained knowledge and works best for common, well-understood tasks.

The maximum amount of text, measured in tokens, that a model can consider at one time, including the prompt and its response. Larger windows allow longer documents and conversations.

A pattern that retrieves relevant documents from an external knowledge source and adds them to the prompt, so the model answers using current, domain-specific information without retraining.

A numeric vector representation of text, images, or other data that captures meaning, so items with similar meaning sit close together. Embeddings enable semantic search and retrieval.

A data store optimized for storing embeddings and quickly finding the most similar vectors to a query. It is a common backing service for retrieval-augmented generation and semantic search.

Further training of a pretrained foundation model on a smaller, task-specific dataset so it adapts to a particular domain, style, or behavior. It is more costly than prompting alone.

When a generative model produces confident but incorrect or fabricated information. It is a key reliability risk and is often mitigated with retrieval, grounding, and human review.

Systematic unfairness in model outputs, often arising from skewed or unrepresentative training data. It can cause discriminatory outcomes and must be measured and mitigated.

The principle that AI systems should treat individuals and groups equitably and avoid discriminatory outcomes. It is assessed through metrics, testing, and diverse data.

The degree to which people can understand why an AI system produced a given output. It supports trust, debugging, auditing, and accountability in decisions affecting users.

A set of principles and practices for building AI that is fair, transparent, safe, accountable, and privacy-respecting. It guides design, testing, and monitoring across the model lifecycle.

A design where people review, approve, or correct AI outputs at key points. It reduces risk in high-stakes decisions and supports accountability and quality control.

Configurable controls that constrain model behavior, such as content filters, topic restrictions, and blocked outputs. They help enforce safety, policy, and brand requirements in applications.

Automated or human review that detects and filters harmful, offensive, or policy-violating content in inputs and outputs. It protects users and supports safe deployment of generative applications.

Protecting personal and sensitive information throughout collection, storage, and processing, including limiting what is shared with AI services and honoring consent and retention rules.

Encoding data so only authorized parties can read it, both while stored and while in transit. It is a core safeguard for protecting sensitive data used by AI systems.

Policies and mechanisms that determine who or what can view or use specific resources. Least-privilege access limits exposure of sensitive data and model endpoints.

The framework for managing user identities and their permissions across systems. In AI workloads it governs who can invoke models, access data, and change configurations.

A chronological record of system activity, such as who accessed data or invoked a model and when. It supports investigations, compliance reporting, and accountability.

The policies, roles, and oversight processes that direct how AI systems are approved, monitored, and retired. It aligns AI use with legal, ethical, and organizational requirements.

A training method that uses human rankings of model outputs to shape a reward signal, then tunes the model toward responses people prefer. It improves helpfulness and alignment.