NLP covers a wide range of tasks that predate the current wave of AI. Detecting the language a message is written in, extracting a phone number or a date from a sentence, working out whether a review is positive or negative, grouping documents by topic, and translating between languages are all classic NLP problems. Many of these were solved reasonably well with statistical methods long before large language models arrived.
What changed recently is generality. Instead of building a separate system for each task, one large model can now handle interpretation and generation together, which is why a modern chat tool can understand a question, find the relevant information and write a reply in a single flow. The older task-specific techniques have not disappeared; they often sit alongside the model doing jobs that need to be precise and cheap.
For a small business, the value of NLP is that customers no longer have to phrase things your way. Someone can type do you do same day callouts near me and get a useful answer rather than a no results page. Clerkzo uses that to answer visitor questions from your own site content and to pull structured details such as preferred time and service type out of an ordinary conversation.
Related terms
Browse all 142 terms- Data Processing Agreement (DPA)A data processing agreement (DPA) is a contract between a data controller and a data processor that sets out how the processor may handle personal data on the controller's behalf, what safeguards apply, and what happens when the arrangement ends.
- Large language model (LLM)A large language model (LLM) is an AI system trained on very large amounts of text to predict the next piece of language in a sequence. That single capability lets it answer questions, summarise documents, write code and hold a conversation.
- Machine LearningMachine learning is an approach to building software where a system learns patterns from examples rather than following rules a developer wrote by hand. The resulting model makes predictions on data it has not seen before.
- Training DataTraining data is the collection of examples an AI model learns from during training. Its content, quality and coverage shape what the resulting model knows and how it behaves.