Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. Powers chatbots, translation, sentiment analysis, and text summarization.
Why it matters
NLP is the reason machines can finally handle everyday human language instead of rigid commands. It powers the tools you use daily: autocomplete, spam filters, voice assistants, and chatbots. It matters because it bridges the gap between how people naturally speak and how computers process information. Without NLP, you'd still be clicking through menus instead of simply asking for what you want in plain words.
In practice
You type 'find cheap flights to Rome next month' into a travel app. NLP breaks that sentence apart, recognizing 'Rome' as a destination, 'next month' as a date range, and 'cheap' as a price preference. The app returns relevant results without you filling in a single dropdown. That everyday convenience, understanding a messy human sentence and acting on it, is NLP quietly doing its job in the background.
Related terms
Artificial Intelligence (AI)
The simulation of human intelligence by computer systems, including learning, reasoning, and self-correction. Modern AI is primarily powered by machine learning and neural networks.
Context Window
The maximum amount of text (measured in tokens) an AI model can consider at once. Larger context windows allow the model to reference more information in a single conversation.
Large Language Model (LLM)
An AI model trained on vast amounts of text data that can generate, summarize, translate, and analyze human language. Examples include GPT-4, Claude, Gemini, and Llama.
Prompt
The text instruction you give to an AI model to generate a response. Prompt quality directly impacts output quality — better prompts yield dramatically better results.
Token
The basic unit of text that AI models process — roughly 3/4 of a word in English. 'Unbelievable' is 3 tokens. Token limits determine how much text a model can process at once.
Tokenizer
The component that splits text into tokens (sub-word units) before feeding it to an AI model. Different models use different tokenizers — affecting how they count input length, handle multilingual text, and process code.