NLP: How Software Understands Human Language

What it is

Natural language processing is the field of AI that deals with reading, understanding, and generating human language. Sentiment analysis, entity extraction, document classification, machine translation, and chatbots are all NLP applications. LLMs are the most capable NLP systems today, but the field includes a wide range of more focused tools and techniques. Most products that deal with text in any way are touching NLP.

How it works

Traditional NLP relied on rules and statistical models. Modern NLP uses transformers and large pre-trained models that understand context and meaning far better than older approaches. For many tasks you can use a pre-trained model directly. For domain-specific needs you fine-tune or use RAG to ground the model in your data. The right approach depends on how specialised your language needs are.

Real example

An insurance company processes thousands of claim documents per day. An NLP pipeline extracts key entities (claimant name, policy number, incident date, damage type), classifies the claim, and routes it to the right team. What used to take hours of manual reading now takes seconds. The same pattern works for legal, finance, and any document-heavy workflow.

When you need it

• You are processing large volumes of text (documents, emails, reviews, support tickets).
• You need to extract structured information from unstructured text.
• You are building anything that involves understanding what a user typed or said.