Natural Language Processing

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.

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.

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.

The transformer is a neural network architecture introduced in the 2017 paper "Attention Is All You Need" that revolutionized natural language processing. Unlike recurrent networks, transformers process entire sequences in parallel using a self-attention mechanism, which allows them to capture long-range dependencies efficiently. Virtually all modern LLMs, including GPT and Claude, are built on the transformer architecture.

Orchestration is the automated coordination of multiple services, tasks, or systems to execute a complex workflow. An orchestrator acts as a central controller that manages the sequence, parallelism, error handling, and retries of individual steps. In the context of AI agents, orchestration involves chaining LLM calls, tool use, and decision-making steps; in DevOps, it coordinates container deployment, scaling, and service discovery.

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.