Generative AI refers to artificial intelligence systems capable of creating new content (text, images, audio, etc.) that resembles human-created work. Built primarily on deep learning architectures, especially transformer models and diffusion models. Key attribute is the ability to produce novel outputs rather than simply classifying or predicting existing data.

# Transformer Architecture

Architectural foundation for modern large language models. Introduced in "Attention Is All You Need" (2017). Relies on self-attention mechanisms to process sequential data in parallel rather than recurrently. Enables efficient training on massive datasets and effective capturing of long-range dependencies in data.

# Large Language Models (LLMs)

Text-based generative AI systems trained on vast corpora of human-written text. Examples include GPT-4, Claude, LLaMA, and PaLM. These models demonstrate emergent capabilities like reasoning, coding, and contextual understanding that weren't explicitly designed. Core technology driving the recent explosion in generative AI applications.

# Multimodal Generative AI

Systems that can process and generate multiple types of data (text, images, audio, video). Examples include GPT-4V, Gemini, Claude Opus, and DALL-E 3. These models can understand relationships between different modalities, allowing for more natural and comprehensive AI interactions.

# Training Methodologies

Approaches for developing generative AI, including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and constitutional AI (CAI). These methods shape model behavior beyond raw pattern recognition to align with human preferences and reduce harmful outputs.

# Diffusion Models

Class of generative models that learn to reverse a gradual noising process. Foundational to modern image generation systems like Stable Diffusion and DALL-E. Work by iteratively denoising random patterns into coherent outputs based on text prompts or other conditioning information.

# Generative AI Applications

Real-world implementations of generative AI across domains: content creation, coding assistance, education, healthcare diagnostics, scientific research, creative arts, business analytics, and personalized services. Represents the practical value and societal impact of these technologies.

# Creative Tools & Interfaces

Software that makes generative AI accessible to end users. Includes text-to-image platforms (Midjourney, DALL-E), writing assistants (ChatGPT, Claude), code generators (GitHub Copilot), and design tools (Adobe Firefly). These interfaces translate user intent into prompts the AI can understand.