Natural Language Processing, or NLP, is the branch of AI that lets computers read, understand, and work with human language, whether written or spoken. It is what turns messy, free-form sentences into something software can act on.
The challenge is that human language is full of ambiguity. “I saw her duck” could mean a bird or a dodge, and people sort that out instantly from context. NLP is the work of teaching machines to do the same. It powers everyday tools you already use: the spam filter reading your email, the search box that understands a typo, and the assistant that answers a spoken question. Specific jobs within it include named entity recognition, which finds names and dates in text, and sentiment analysis, which judges whether a message is positive or negative.
Modern NLP got a big lift from the large language model, which reads and writes text with surprising fluency. But the field is broader than that one approach, covering everything from simple keyword rules to deep models. For a fixed job like flagging a phrase, a plain rule is faster, cheaper, and easier to debug than a giant model.
Language is also a moving target. Slang, sarcasm, typos, and mixed Dutch and English in one message all throw a system off, and a model trained on formal text will struggle with how people actually write. So results need checking against your own data, not a benchmark. Treat NLP as a way to make a pile of text workable, not as a guarantee it read every line the way you would.
At TopDevs we apply NLP so a client’s emails, reviews, and documents become searchable, sortable data instead of a pile of text nobody has time to read.
