AI Training Glossary

The practice of crafting and optimizing inputs (prompts) to AI models to achieve desired outputs and behaviors.

A machine learning approach where AI models learn to perform new tasks from only a small number of training examples.

The ability of AI models to perform tasks or understand concepts without any specific training examples, relying solely on pre-existing knowledge.

The AI capability to identify what a user wants to accomplish from their spoken or written input, even when expressed in varied ways.

An AI technique where knowledge gained from training on one task is applied to improve performance on different but related tasks.

The AI capability to comprehend human language with its nuances, context, and intent, going beyond simple word recognition.

The ability of AI models to learn and adapt to new tasks by processing examples and instructions provided within the input prompt itself.

A prompting technique that guides AI models to break down complex problems into step-by-step reasoning processes, improving accuracy and transparency.

An AI architecture that enhances model responses by retrieving relevant information from external knowledge bases before generating answers.

The process of further training a pre-trained AI model on specific data to adapt it for particular tasks, domains, or behaviors.

The ability of AI systems to maintain awareness of conversation history, user information, and situational factors across multiple interaction turns.

A search approach that understands the meaning and context of queries rather than just matching keywords, enabling more relevant results.

Mathematical representations of text, images, or other data as arrays of numbers that capture semantic meaning and enable similarity comparisons.

Lingua's proprietary methodology for structuring AI prompts specifically for natural, reliable voice interactions.

The progression of stages businesses go through when implementing AI, from experimentation to full-scale operational integration.

The practice of designing natural, effective dialogue flows between humans and AI systems, focusing on user goals and conversational principles.

The use of voice-based AI interfaces to facilitate shopping, customer service, and commercial transactions through natural conversation.

When AI models generate information that sounds plausible but is factually incorrect or not grounded in their training data or provided context.

The time delay between a user input and the AI system's response, critical for natural-feeling voice conversations.

Safety mechanisms and constraints that prevent AI systems from generating harmful, inappropriate, or off-brand content.