Reliant's AI Glossary

The percentage of correct predictions (For example: percentage of cancer cases correctly identified by an AI system)

Systems that go beyond simple Q&A to actively plan, decide, and execute multiple steps to solve complex problems.

The method the system uses to learn or make decisions. Every piece of code follows an algorithm.

The broad field of creating systems capable of performing tasks that typically require human intelligence.

Systematic errors in AI predictions.

The design of systems that provide the AI with the right information at the right time.

A structured collection of data used for training or testing.

A sophisticated subset of Machine Learning that uses multi-layered neural networks.

The ability to trace and understand how an AI model reached a specific decision.

A specialized series of automated steps designed to identify and pull specific data points from text.

The AI-powered process of refining a broad search down to the most relevant results.

The ability of an AI to perform consistently across new, unseen scenarios.

AI models capable of creating new content, such as text, images, or molecular structures.

Verified, accurate data used as the absolute standard for training and evaluation.

The technical infrastructure or 'fence' wrapped around a Large Language Model.

The action of a trained model applying its knowledge to new, unseen data to make a decision.

A type of AI model trained on massive text datasets to understand and generate human-like language.

A subset of AI where algorithms improve through experience rather than explicit programming.

The specific mathematical engine trained to recognize patterns and make predictions (distinct from the full software 'System').

The branch of AI focused on interpreting and generating human language.

Precision measures exactness (low noise); Recall measures completeness (no missed signals).

The art of crafting precise instructions to guide Large Language Models.

AI models built and owned exclusively by a company, trained on unique datasets to solve specific challenges.

The process of identifying specific information (e.g., study results, author lists) within documents and organizing it into a standard format.

Structured Data is organized in fixed formats like databases; Unstructured Data is free-form like PDFs or notes.

A reasoning methodology where the AI explores multiple logical paths (a 'tree') before selecting the best answer.