Natural Language Processing

Chain of Thought (CoT) is a prompting technique that encourages an LLM to break down complex reasoning into intermediate steps before arriving at a final answer. By explicitly reasoning through each step, models achieve significantly better accuracy on math, logic, and multi-step problems. Extended thinking and "thinking" tokens in models like Claude represent a built-in form of chain-of-thought reasoning.

A diffusion model is a type of generative AI that creates data by learning to reverse a gradual noise-adding process. During training, the model learns to progressively denoise random noise into coherent outputs like images, audio, or video. Diffusion models power tools like Stable Diffusion, DALL-E, and Midjourney, and have become the dominant architecture for high-quality image generation.

Computer vision is a field of AI that trains machines to interpret and understand visual information from images and videos. Applications include object detection, facial recognition, autonomous driving, and medical image analysis. Modern computer vision leverages deep learning models like CNNs and vision transformers (ViT), and increasingly integrates with language models in multimodal AI systems.

An embedding is a dense numerical vector representation of data — such as text, images, or code — in a high-dimensional space where semantically similar items are positioned closer together. Embeddings are fundamental to semantic search, recommendation systems, and RAG pipelines. They are generated by specialized models and typically stored in vector databases for efficient similarity lookups.

Multimodal AI refers to models that can process and generate multiple types of data — such as text, images, audio, and video — within a single system. Models like GPT-4o and Claude can accept both text and image inputs, enabling use cases like visual question answering, document analysis, and UI understanding. This convergence is blurring the lines between previously separate AI disciplines.

In the context of AI language models, a token is the basic unit of text that a model processes — typically a word, subword, or character depending on the tokenizer. LLM pricing, context windows, and rate limits are all measured in tokens. Understanding tokenization is essential for optimizing costs and staying within model context limits when building AI-powered applications.