Overview
The process of teaching an AI model to recognise patterns by exposing it to large datasets and adjusting its parameters.
More in Artificial Intelligence
AI Orchestration Layer
Infrastructure & OperationsMiddleware that manages routing, fallback, load balancing, and model selection across multiple AI providers to optimise cost, latency, and output quality.
Few-Shot Learning
Prompting & InteractionA machine learning approach where models learn to perform tasks from only a small number of labelled examples, often achieved through in-context learning in large language models.
AI Interpretability
Safety & GovernanceThe degree to which humans can understand the internal mechanics and reasoning of an AI model's predictions and decisions.
AI Red Teaming
Safety & GovernanceThe systematic adversarial testing of AI systems to identify vulnerabilities, failure modes, harmful outputs, and safety risks before deployment.
BLEU Score
Evaluation & MetricsA metric for evaluating the quality of machine-generated text by comparing it to reference translations or texts.
Speculative Decoding
Models & ArchitectureAn inference acceleration technique where a small draft model generates candidate token sequences that are verified in parallel by the larger target model.
Expert System
Infrastructure & OperationsAn AI program that emulates the decision-making ability of a human expert by using a knowledge base and inference rules.
Zero-Shot Prompting
Prompting & InteractionQuerying a language model to perform a task it was not explicitly trained on, without providing any examples in the prompt.