NLP

NLP stands for Natural Language Processing: the branch of artificial intelligence that deals with human language — reading text, understanding intent, translating, summarizing, and generating replies.

It powers spell-check, spam filters, search, chatbots, voice assistants, and large language models (LLMs). Classic NLP broke language into tokens, grammar, and entities; modern systems often use neural networks and transformers trained on huge text corpora. The goal is the same: make machines useful with words people actually write and say.

A real-life example of NLP

You ask your phone, “What’s the weather this weekend?” Speech recognition turns sound into text; NLP figures out you want a forecast for Saturday–Sunday, not a Wikipedia article about “weekend.” Voice search depends on that pipeline — see What Is Voice Search?.

NLP isn’t perfect: slang, sarcasm, and domain jargon still trip systems up. That’s why human review and clear training data matter when you put language AI in a product — and why techniques like RAG help keep answers grounded in your own docs.