LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that keeps a pre-trained model frozen while training small low-rank matrices. This guide covers pronunciation, the rank decomposition math, step-by-step usage with Hugging Face PEFT, QLoRA variants, comparison with full fine-tuning, memory savings, and common implementation pitfalls.

AGI (Artificial General Intelligence) refers to AI capable of performing a broad range of intellectual tasks at or beyond human level. Learn its definition, pronunciation, differences from ASI and narrow AI, and the timelines predicted by leading AI labs.

Multimodal AI processes multiple data types — text, images, audio, and video — in a single unified model. This guide explains how it works, covers flagship models like GPT-4o, Gemini, and Claude, and shows practical use cases.

RLHF (Reinforcement Learning from Human Feedback) trains a reward model from human preferences and uses it to fine-tune LLMs. This complete guide covers pipeline, math, PPO vs DPO, and how ChatGPT and Claude use RLHF.

A complete guide to Constitutional AI: definition, how it works, advantages, disadvantages, comparisons, and real-world use cases.

Transformer is the neural network architecture that powers modern generative AI, including ChatGPT and Claude. This guide explains pronunciation, the attention mechanism, differences from RNN and CNN, leading models (BERT, GPT, LLaMA), and hands-on Python examples.

Fine-tuning is the process of further training a pre-trained AI model on a smaller, domain-specific dataset to adapt it for specific tasks. In 2026, parameter-efficient methods like LoRA and QLoRA dominate due to their cost-effectiveness and ability to reduce trainable parameters by 10,000x.