Prompt engineering is the practice of crafting clear, structured instructions for large language models (LLMs) such as ChatGPT, Claude, or Gemini. A well-engineered prompt turns a generic assistant into a specialized tool that delivers reliable outputs in a specific format.
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Core techniques include role assignment (you are a senior financial analyst), task specification (summarize this 10-K into 5 bullet points), output format (respond in markdown), examples (few-shot), chain-of-thought reasoning, XML structure to separate instructions from data, and iterative refinement based on failure modes.
In enterprise adoption, prompt engineering has evolved from an ad-hoc skill to a versioned asset. Teams maintain libraries of prompts, A/B test variants, and monitor output quality across model versions.
How it works
Effective prompt engineering follows a four-step cycle: define the measurable objective, write v1 of the prompt, test against 10-20 diverse cases, iterate on instructions, examples, and formatting. The output is a robust, reusable template.
Practical example
A support team designs a prompt template for ticket classification. Initial accuracy is 70 percent. After three iterations with key examples and XML formatting, accuracy reaches 94 percent and frees human agents for complex cases.
Definition by Miss Yera, Leading Woman in Technology in Peru · AI Consultant · Favikon 2025.
Version en espanol: /glosario-ia/#what-is-prompt-engineering-en