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Plain-language AI glossary

Term 41ApplicationsMeaning / context / connections

Applications / Definition

Natural Language Processing (NLP)

The branch of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.

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MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

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UsesWhere the concept becomes useful.

01 / Plain-language context

How Natural Language Processing (NLP) works.

NLP encompasses everything AI does with text: understanding meaning (comprehension), extracting information (parsing), generating text (writing), and converting between formats (translation, summarization). Before LLMs, NLP required separate specialized models for each task. Now, a single LLM handles most NLP tasks through prompting.

The NLP pipeline in a typical application: (1) Input processing (tokenization, language detection), (2) Understanding (intent classification, entity extraction), (3) Processing (search, retrieval, reasoning), (4) Generation (response creation, formatting). Modern AI APIs handle all of this in a single call, but understanding the pipeline helps you debug issues and optimize prompts.

For builders, NLP is what most AI applications actually do. Every chatbot, search engine, content generator, data extractor, and text analyzer is an NLP application. The key skill is knowing which NLP sub-task your feature requires (classification? extraction? generation? search?) and choosing the right model and approach for it.

02 / Practical uses

Where it helps.

  1. 01Text classification and categorization
  2. 02Language translation
  3. 03Document summarization
  4. 04Intent detection for chatbots
  5. 05Information extraction from text
03 / Keep exploring
Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.Sentiment AnalysisAn NLP technique that determines the emotional tone of text, classifying it as positive, negative, neutral, or more granular emotions.Named Entity Recognition (NER)An NLP technique that identifies and classifies named entities in text, such as people, organizations, locations, dates, and monetary values.Semantic SearchA search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.