NLP & LANGUAGE

LLM (Large Language Model)

NLP & Language

Definition

An LLM is a large-scale language model trained on enormous amounts of text, capable of understanding and generating natural language with advanced reasoning abilities. Claude, GPT-4, and Gemini are LLMs. For SMEs, LLMs are the most versatile tool available: they can write, translate, analyze, code, and reason about complex problems.

Related terms

AI Fundamentals

Language Model

A language model is an AI system trained on enormous amounts of text to understand and generate natural language. Large language models (LLMs) such as GPT and Claude can write texts, answer questions, translate, and reason about complex problems. For businesses, they represent versatile assistants for any text-based task.

AI Fundamentals

Foundation Model

A foundation model is a large-scale AI model trained on vast and diverse data that can be adapted to multiple specific tasks. GPT-4, Claude, and Gemini are foundation models. Instead of building a model for every need, companies can start from a foundation model and specialize it for their requirements through fine-tuning or prompt engineering.

NLP & Language

Prompt Engineering

Prompt engineering is the art and science of writing effective instructions to get the best results from AI models. It includes techniques such as few-shot learning, chain-of-thought, system roles, and output structuring. It is a key skill for any company using AI: a good prompt can make the difference between a useless and an excellent output.

EXPLORE

More terms in NLP & Language

NLP (Natural Language Processing)

NLP is the field of AI concerned with the interaction between computers and human language. It includes tasks such as sentiment analysis, entity extraction, machine translation, and text generation. For SMEs, NLP enables automating the analysis of emails, customer reviews, legal documents, and internal communications.

Sentiment Analysis

Sentiment analysis is the NLP technique that automatically determines the emotional tone of a text: positive, negative, or neutral. Companies use it to monitor customer reviews, analyze brand sentiment on social media, and quickly identify satisfaction issues. It can be applied to thousands of texts in seconds.

Named Entity Recognition (NER)

NER is an NLP technique that automatically identifies and classifies entities such as names of people, companies, locations, dates, and monetary amounts within a text. In business, it is used to extract key information from contracts, invoices, emails, and legal documents, saving hours of manual data entry work.

Tokenization

Tokenization is the process of splitting a text into smaller units (tokens) that the AI model can process. Different models use different strategies: some split by words, others by subwords. Understanding tokenization helps estimate API costs (calculated per token) and optimize prompts to stay within the context window limits.

BERT

BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that understands the context of a word by looking both left and right in the text. It is particularly effective for comprehension tasks such as classification, question answering, and semantic search. Many enterprise search systems use BERT behind the scenes.

Text Classification

Text classification is the task of automatically assigning one or more categories to a document. Practical applications include: routing support tickets by priority, cataloging emails by department, classifying accounting documents by type. Modern models achieve accuracy above 95% on many business classification tasks.

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