Your quick reference for artificial intelligence terminology, techniques, and real-world applications.
Key AI terms from ML and NLP — supervised learning, fine-tuning, tokens, embeddings, and more.
How AI models are trained and improved — backpropagation, RLHF, quantization, RAG, and more.
Where AI is used in the real world — healthcare, finance, creative work, customer support, and more.
LLMs, diffusion models, CNNs, GANs, transformers, and other AI architectures explained.
How to write effective prompts — zero-shot, few-shot, chain-of-thought, and structured prompts.
Underlying concepts — loss functions, attention, temperature, perplexity, and accuracy metrics.
AI refers to computer systems designed to perform tasks that normally require human intelligence — including learning, reasoning, problem-solving, perception, and language understanding. Modern AI is powered by machine learning, where models learn patterns from data rather than following explicit rules.
Traditional ML models are built for one specific task (e.g., classify spam). Large Language Models are general-purpose — trained on massive text corpora to understand and generate human language across countless tasks.