AI & Automation
What is Natural Language Processing (NLP)?
Definition
The branch of AI focused on enabling machines to understand, interpret, and generate human language — the foundational discipline behind LLMs, chatbots, and text analytics.
In more detail
NLP encompasses a range of language tasks: text classification (is this email spam or legitimate?), named entity recognition (who and what is mentioned?), sentiment analysis (is this review positive?), translation, summarisation, and question answering. These tasks are the building blocks of most business AI features.
Modern NLP is dominated by transformer-based models — BERT, GPT, Claude, Gemini — that learn language patterns from enormous training corpora rather than hand-coded linguistic rules. The shift to transformers around 2017 produced step-change improvements in nearly every NLP benchmark.
In practical AI applications, NLP is what allows systems to understand what a user meant (intent), what a document contains (extraction), and what response to generate (generation). The line between 'NLP system' and 'LLM application' has blurred significantly — most modern NLP work is now LLM-based.
Why it matters
NLP is the technical foundation beneath most business AI features. Knowing what's possible — and what the realistic limitations are — helps non-technical leaders scope AI projects accurately and avoid both over-investment and under-investment.
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