Fine-tuning

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.

An embedding is a dense numerical vector representation of data — such as text, images, or code — in a high-dimensional space where semantically similar items are positioned closer together. Embeddings are fundamental to semantic search, recommendation systems, and RAG pipelines. They are generated by specialized models and typically stored in vector databases for efficient similarity lookups.

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.

In AI, hallucination refers to when a language model generates confident-sounding but factually incorrect or fabricated information. This occurs because LLMs predict statistically likely text rather than retrieving verified facts. Mitigation strategies include RAG, grounding responses in source documents, structured output validation, and using temperature settings to reduce creative deviation.

A context window is the maximum amount of text (measured in tokens) that an LLM can process in a single interaction, encompassing both the input prompt and the generated output. Larger context windows allow models to handle longer documents, maintain extended conversations, and reason over more information at once. Context window sizes have grown rapidly — from 4K tokens in early GPT models to over 1M tokens in current models like Claude.

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.