Natural Language Processing (NLP)
AI technology for understanding human language. Underpins how AI interprets and responds to queries.
What is Natural Language Processing?
Natural language processing is the branch of artificial intelligence concerned with getting computers to read, interpret and produce human language. It covers tasks such as splitting text into tokens, identifying parts of speech, recognising named entities (people, companies, products, places), determining sentiment, classifying intent, extracting relationships, translating between languages and summarising documents. Modern NLP is dominated by neural networks, and large language models are its most visible product.
Search engines have used NLP for years to understand queries and documents beyond keyword matching: recognising that two phrasings ask the same thing, that a page is about a particular entity, or that a query has a local or transactional intent. Google's BERT and MUM models were milestones in applying this to search. AI answer engines take the same capabilities further, using a language model to interpret a conversational request, rewrite it into retrieval queries, read the retrieved passages and compose an answer.
For content, the practical consequence is that engines understand meaning, entities and structure. Naming the subject explicitly, using consistent entity names, writing clear sentences with one idea each, and organising a page under descriptive headings all make it easier for NLP systems to identify what a passage is about and whether it answers a given question.
Why it matters for AI search
Every step of an AI answer, from interpreting the prompt to choosing which passage to quote, is an NLP task, so content that is easy for NLP systems to parse is content that gets retrieved and cited. That means plain language, explicit entity names, clear structure and unambiguous claims. It also explains why brand consistency matters: entity recognition works best when the same name and description appear everywhere, and it breaks down when a brand is described differently on every page.
Related terms
LLM (Large Language Model)
A type of AI trained on vast amounts of text data to understand and generate human-like language (e.g., GPT-4, Claude 3).
Semantic Search
Search focused on the meaning and intent behind words rather than simple keyword matching.
Embedding
A numerical representation of text that AI uses to understand meaning and find similar content.
Conversational Query
Natural language questions users ask AI assistants, as opposed to keyword-based search queries. AEO optimizes for these.
Frequently asked questions
How is NLP different from an LLM?+
NLP is the broad field of computational language understanding and generation. A large language model is one type of NLP system, trained at very large scale, that now handles many NLP tasks in one model. Older NLP used separate models for parsing, entity recognition, sentiment and so on.
How does NLP affect how I should write for AI search?+
Write so that a system can identify the topic, the entities and the answer without guessing: name things explicitly, keep one idea per sentence, use headings that state the question, and keep brand and product names consistent everywhere they appear.