What is NLP?

NLP stands for natural language processing, and it’s the field of technology that lets computers read, interpret, and write human language. It’s the umbrella that today’s large language models belong under – but the field is older and broader than that. Understanding it gives you perspective on what today’s AI is actually a continuation of.

What NLP is

Human language is messy for a computer. The same thing can be said a hundred different ways, words mean different things in different contexts, and a lot is left unsaid. NLP is the umbrella term for the techniques that nonetheless get computers to work with language – reading in text, interpreting what it means, sorting it, and formulating language back.

So the field isn’t about a single product but a whole toolbox. Everything from spell-checking and translation to search engines and chat interfaces is built on some form of NLP. The goal is always the same: to bridge the gap between how people express themselves and how computers process information.

Classic tasks

Long before today’s language models, NLP was already solving concrete problems, often with its own model built for each task. Some recurring examples:

• Text classification. Sorting text into categories – for example, determining what subject a document is about.
• Named entity recognition. Finding and tagging the names of people, companies, places, and amounts in running text.
• Sentiment analysis. Determining whether a text expresses something positive, negative, or neutral, for example in customer reviews.

Each of these tasks was traditionally built as a separate solution, trained and fine-tuned for its specific purpose. It worked, but it required its own model and its own maintenance per problem.

How the LLM changed the field

Here’s the big shift. A large language model, or LLM, is the latest generation within NLP, and it has redrawn the map. Where you once built a model per task, a single LLM can often handle classification, summarization, translation, named entity recognition, and much more – guided by instructions in plain text instead of separate training.

That has meant many older specialized models can be replaced by one and the same tool. The barrier to getting started has dropped considerably: instead of training your own model, you can often just ask a language model to do the task directly. Older, more specialized methods still have their advantages – they can be faster, cheaper, and more predictable for a narrow, high-volume task – but across the breadth of language tasks, the LLM has become the first choice.

A concrete business example

To make it concrete: picture a company that gets hundreds of emails a day into a shared inbox. An NLP solution can read every incoming email and automatically route it to the right department – finance, support, sales – based on the content, not on rigid rules about sender or keywords.

That saves time, reduces the risk of cases ending up in the wrong place, and gives customers faster responses. The same basic idea comes up everywhere text needs to be interpreted or produced: summarizing documents, pulling data out of free text, analyzing reviews, or powering an internal assistant.

Why the term is worth knowing

NLP, then, isn’t a product you buy, but a category of techniques. That’s useful to know, because it helps you see through marketing. When a vendor talks about their tool “understanding language,” “reading documents,” or “analyzing text,” it’s some form of NLP behind it – whatever brand name gets put on it.

With that understanding, it gets easier to ask the right questions. Instead of getting stuck on buzzwords, you can ask what the technology is actually going to do with your text: sort, summarize, extract, or answer? And how is doing that well measured? In other words, NLP isn’t a question for the future, but technology that’s already delivering everyday value – and one that gets easier to buy once you know what the name stands for. Want to know how it could apply to your business? Read more about our AI work or get in touch.