NLP & LANGUAGE

Prompt Engineering

NLP & Language

Definition

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.

Related terms

AI Fundamentals

Prompt

A prompt is the instruction or question provided to an AI model to obtain a response. The quality of the prompt largely determines the quality of the output. Writing effective prompts (prompt engineering) is a key skill for getting the most out of AI tools in business, from report generation to content creation.

AI Fundamentals

Few-Shot Learning

Few-shot learning is a technique in which you provide the AI model with a few examples of the desired task directly in the prompt. For instance, by showing 3 emails classified as 'urgent' or 'normal', the model learns the pattern and classifies subsequent emails. It is a quick and practical way to customize AI without the need for fine-tuning.

AI Fundamentals

Chain-of-Thought

Chain-of-thought is a prompt engineering technique that guides the AI model to reason step by step before providing an answer. By explicitly asking 'reason step by step', you get more accurate responses on complex problems such as financial analyses, calculations, and strategic decisions. It is particularly useful for business tasks requiring logical reasoning.

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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