Understanding AI and machine learning terminology is the first step to effectively working with modern AI systems. This guide explains the most common terms used in the field, from ML models to prompt engineering.
Machine Learning Models are algorithms trained on data to make predictions or decisions without being explicitly programmed for every task. They learn patterns from training data, which can be labeled (input-output pairs) or unlabeled (raw data without predefined answers).
Training involves feeding data to a model and adjusting its parameters to minimize errors. Data split refers to dividing the dataset into training, validation, and test sets to evaluate performance. MLOps is the practice of deploying and maintaining machine learning models in production.
Large Language Models (LLMs) are a type of AI model trained on vast amounts of text to generate human-like responses. They can answer questions, write essays, and even code. However, they sometimes produce hallucinations—confident but incorrect statements.
Fine-tuning adapts a pre-trained model to a specific task using a smaller dataset. Prompt engineering involves crafting input prompts to get desired outputs. Common techniques include zero-shot prompting (no examples), few-shot prompting (a few examples), and chain-of-thought prompting (step-by-step reasoning).
Knowledge cutoff refers to the date when an LLM's training data was collected; the model cannot know events after that time. Multimodal models can process multiple data types, such as text and images.
Mastering these terms provides a solid foundation for anyone entering the AI field, from developers to business leaders.