Prompt engineering

What is prompt engineering?

Prompt engineering refers to the practice of designing, structuring, and optimizing instructions (prompts) submitted to a model of artificial intelligence to obtain the most relevant and accurate results possible. It is a skill halfway between communication, logic and knowledge of how LLMs (Large Language Models) work.

Why has prompt engineering become a key skill?

The quality of an AI output depends directly on the quality of the prompt. A poorly worded prompt produces generic, inaccurate, or unusable responses. Conversely, a well-structured prompt (with context, role, output format, constraints) makes it possible to fully exploit the capabilities of models like Claude, GPT-4 or Gemini. In a professional context (copywriting, code, design, SEO), prompt engineering multiplies productivity.

What are the basic techniques of prompt engineering?

The fundamental techniques include: defining a role (“You are an SEO expert...”), providing precise context, specifying the expected output format (list, array, JSON), giving examples (few-shot prompting), breaking down complex tasks into steps (chain-of-thought), and iterating by gradually refining. For advanced cases, we use the system prompt, conditional statements and dynamic variables, in particular via tools such as the protocol MCP.

What is the link between prompt engineering and SEO/AEO?

Prompt engineering directly influences AEO (Answer Engine Optimization): understanding how AIs generate their responses helps to structure your web content to be cited as a source. Likewise, SEO professionals use prompt engineering to automate content production, generate tag variants, analyze data, or produce schema descriptions. It is a transversal skill that amplifies all digital practices.