Computer Vision

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.

Fine-tuning is the process of further training a pre-trained AI model on a smaller, domain-specific dataset to adapt it for a particular task. Instead of training from scratch, fine-tuning adjusts existing model weights, which is significantly cheaper and faster. Common approaches include full fine-tuning, LoRA (Low-Rank Adaptation), and instruction tuning for aligning model behavior with specific requirements.

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.

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.

Natural Language Processing (NLP) is a branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers applications like chatbots, translation services, sentiment analysis, and text summarization. Modern NLP has been transformed by transformer-based models, which achieve remarkable performance on tasks that previously required extensive hand-crafted rules.

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