Natural language processing is the branch of AI focused specifically on enabling computers to understand, interpret, and generate human language. It's a broader, older field than large language models. LLMs are currently the most capable and widely used approach to NLP, but the discipline covers a range of distinct tasks well beyond generating conversational text.

The core NLP tasks that show up in real applications

Classification

Categorizing text into predefined categories (sentiment analysis, spam detection, support ticket routing) is one of the most common and mature NLP applications, often solvable with simpler supervised learning techniques rather than requiring a large language model.

Named entity recognition

Identifying specific entities within text (people, organizations, dates, locations, product names) is foundational to practical document processing and information extraction, letting a system pull structured data out of unstructured text.

Semantic search and information retrieval

Understanding the meaning of a query well enough to find relevant content, even without exact keyword overlap, is a core NLP capability underlying modern retrieval systems. We cover it directly in semantic search vs. keyword search.

Machine translation

Translating text between languages while preserving meaning was one of the original driving applications of NLP research. It remains an important real-world use case, now substantially improved by the same transformer architecture that powers modern language models (the transformer was in fact first introduced for translation).

Text generation and summarization

Generating new text, whether a full response or a condensed summary of a longer document, is the category modern LLMs have made dramatically more capable. It's the application most people now associate with "AI," even though it's just one branch of NLP.

Why NLP is a broader category than "using an LLM"

Many real NLP applications, especially classification and entity recognition on well-defined tasks, don't need a large, general-purpose language model at all. A smaller, purpose-built model trained for the task can be faster, cheaper, and just as accurate. Reaching for a large language model for every NLP task is a common and avoidable inefficiency.

Real-world applications worth knowing about

A useful distinction to keep in mind
Not every NLP problem needs the biggest, most general model available. Matching the technique (classification, entity recognition, retrieval, generation) to what the task actually requires is often both cheaper and more reliable than defaulting to a large language model for everything.

How we approach this

We match the NLP technique to the actual task: a purpose-built classifier for well-defined categorization, retrieval for search, a large language model for genuinely open-ended generation. We don't default to the most general, most expensive tool for every language problem.