Machine learning (ML) is a branch of artificial intelligence in which algorithms learn patterns from data — instead of following hard-coded rules — and improve their performance on a defined task as they process more examples. The three core paradigms are supervised learning (labeled data), unsupervised learning (unlabeled data) and reinforcement learning (reward-based interaction). In … Read more
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language models by retrieving relevant documents from an external knowledge base before generating a response. Instead of relying solely on static training data, RAG injects real-time, domain-specific context into the prompt — reducing hallucinations and keeping answers current. This guide walks you through every layer … Read more
An LLM (Large Language Model) is an AI system built on the transformer architecture that processes, understands, and generates human-like text by predicting the next token in a sequence. Trained on trillions of tokens from books, websites, and code repositories, LLMs such as GPT-4, Claude, and Llama 3 power applications ranging from conversational assistants and code … Read more
Artificial intelligence (AI) is the branch of computer science focused on building systems that perform tasks associated with human intelligence — learning, reasoning, planning, perception, and communication. Modern AI spans everything from rule-based expert systems and classical machine learning to deep neural networks and large language models, and it is governed by an evolving regulatory landscape including … Read more