What is Prompt Engineering? - Definition & Meaning

Definition

Prompt engineering is the art and science of designing effective instructions (prompts) for AI language models to generate desired output. It involves structuring, formulating, and optimizing the input you give to an LLM to obtain accurate, relevant, and actionable responses.

Technical explanation

Prompt engineering encompasses various techniques: zero-shot prompting (a direct question without examples), few-shot prompting (the prompt includes several examples of desired input-output pairs), chain-of-thought (CoT) prompting (the model is asked to reason step by step), and system prompts that define the model's role, tone, and constraints. Advanced techniques include ReAct (Reasoning + Acting) where the model alternates between reasoning and executing actions, tree-of-thought for complex problem solving, and constitutional AI for embedding safety guidelines. Prompt templates are often set up parametrically with variables for context, user information, and business-specific data. Prompt evaluation uses A/B testing, human assessment, and automated metrics like BLEU, ROUGE, and G-Eval. Temperature, top-p, and max-tokens are important inference parameters that influence output.

How OpenClaw Installeren applies this

OpenClaw Installeren delivers every AI assistant with optimized system prompts tailored to your use case. During the installation process, we configure the prompt templates for your business context, including tone, language, knowledge base references, and safety rules. You can easily adjust the prompts after installation via the configuration file.

Practical examples

• A system prompt for a customer service chatbot: "You are a friendly customer service agent for [company]. Answer questions exclusively based on the provided knowledge base. If you don't know the answer, refer the customer to support@company.com."
• A few-shot prompt for product classification where the model receives three examples of correctly classified products, then independently categorizes new products correctly.
• A chain-of-thought prompt for a financial analysis assistant that instructs the model to first identify relevant figures, then perform calculations, and finally formulate a conclusion.

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