Definition
Natural language processing (NLP) is the field of computer science and AI concerned with getting computers to analyze, understand and produce human language in text and speech. Search engines, chatbots, translation tools, voice assistants, spam filters and the language models behind ChatGPT, Gemini and Claude are all NLP systems.
The field has gone through three broad eras. Early systems used hand-written grammar rules and dictionaries. From the 1990s, statistical methods learned word patterns from large text collections. Since the mid-2010s, neural networks have dominated, and the transformer architecture introduced in 2017 led to models such as Google's BERT, which Google began using in Search in 2019 to better understand how words in a query relate to each other, and to the generative large language models used today.
Classic NLP tasks include:
- Tokenization, splitting text into words or sub-word tokens
- Named entity recognition, identifying people, companies, products and places
- Sentiment analysis, judging whether text is positive, negative or neutral
- Classification, sorting text by topic or intent
- Question answering, summarization and translation
- Text generation
Modern LLMs perform most of these tasks with a single model, often from a plain-language instruction instead of a specially trained system for each job. Specialized models still matter where speed and cost count, such as classifying billions of queries or building embeddings for retrieval.
NLP runs through every step of AI search. When someone types or speaks a question, NLP models interpret the intent, entities and constraints in it. Retrieval systems use embeddings to find passages that match the meaning of the query rather than only its exact words, which is the basis of semantic search. Generative models then read the retrieved passages and write an answer. Multilingual models have widened the reach of these products: in October 2025, Google expanded AI Mode to more than 35 additional languages and over 200 countries and territories in total.
NLP is also how AI visibility is measured. Deciding whether an AI answer mentions a brand, which brand is recommended first, and whether the description is favorable are entity recognition and sentiment analysis problems. Ambiguous brand names, abbreviations and product names that are also common words make this harder, which is why careful brand detection matters in any tracking setup.
For content teams, the practical lesson is that writing for NLP systems means writing clearly for people. Google's guidance on generative AI features in Search says you do not need to write in a special way for AI, because its systems understand synonyms and general meaning. What helps machines and readers alike is unambiguous language: name the entities you are talking about, state facts plainly, define terms the first time you use them, keep one idea per paragraph, and use headings that describe what follows. Clear entity salience, meaning it is obvious what a page is mainly about, makes it easier for retrieval systems to match the page to the right questions and for language models to quote it accurately.
Examples of Natural Language Processing (NLP)
- Google's systems understand that 'can you get medicine for someone pharmacy' is about picking up a prescription on someone else's behalf, the kind of preposition-dependent query BERT was introduced to handle.
- A support team uses an LLM to classify 50,000 incoming tickets by product and urgency each week, replacing a keyword rules system that misrouted about a fifth of them.
- An AI visibility tool has to decide whether 'Apple' in an answer about laptops means the company and whether 'Notion' in a productivity answer refers to the app, using the surrounding text to disambiguate.
- A European retailer writes product pages in plain, entity-rich language in German and French and tracks whether AI Mode cites them in answers to German and French prompts.
