AI Agent

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.

A large language model (LLM) is a deep learning model trained on massive text datasets to understand and generate human-like text. LLMs like GPT, Claude, and LLaMA power chatbots, code assistants, and content generation tools. They work by predicting the next token in a sequence based on learned statistical patterns across billions of parameters.

A neural network is a computational model inspired by the human brain, consisting of layers of interconnected nodes (neurons) that process data by adjusting weighted connections during training. Deep neural networks with many layers form the foundation of modern AI, powering everything from image recognition to language understanding. Common architectures include feedforward networks, convolutional networks (CNNs), and transformers.

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.

A vector database is a specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings. They enable fast similarity search, which is critical for RAG systems, recommendation engines, and semantic search applications. Popular vector databases include Pinecone, Weaviate, Qdrant, and pgvector for PostgreSQL.