Large Language Model

Prompt engineering is the practice of crafting and optimizing input instructions to guide AI models toward producing desired outputs. Techniques include few-shot examples, chain-of-thought reasoning, role assignment, and structured output formatting. Effective prompt engineering can dramatically improve the quality, accuracy, and consistency of LLM responses without modifying the underlying model.

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.

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.

Generative AI is a category of artificial intelligence that creates new content — text, images, code, music, or video — rather than just analyzing or classifying existing data. Powered by architectures like transformers and diffusion models, generative AI has transformed software development with tools like GitHub Copilot, Claude, and Cursor. It represents a shift from AI as a classification tool to AI as a creative collaborator.

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.