Prompt Engineering
The process of designing and refining prompts to elicit accurate and relevant responses from AI models.
The process of designing and refining prompts to elicit accurate and relevant responses from AI models.
A model of organizational change management that involves preparing for change (unfreeze), implementing change (change), and solidifying the new state (refreeze).
A set of cognitive processes that include working memory, flexible thinking, and self-control, crucial for planning, decision-making, and behavior regulation.
An AI model that has been pre-trained on a large dataset and can be fine-tuned for specific tasks.
A method in natural language processing where multiple prompts are linked to generate more complex and contextually accurate responses.
The mathematical study of waiting lines or queues.
A psychological model that outlines the stages individuals go through to change behavior, including precontemplation, contemplation, preparation, action, and maintenance.
Model-View-Controller (MVC) is an architectural pattern that separates an application into three main logical components: the Model (data), the View (user interface), and the Controller (processes that handle input).
Large Language Model (LLM) is an advanced artificial intelligence system trained on vast amounts of text data to understand and generate human-like text.