RAG

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 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.

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.

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.

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.

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.