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Prompt Engineering

The practice of designing, refining, and optimizing input text (prompts) to elicit the most accurate and useful responses from AI models.

Prompt engineering is both an art and a science used to communicate effectively with large language models (LLMs). Because models are sensitive to how instructions are phrased, the order of information, and the formatting of the prompt, a well-engineered prompt can significantly improve the quality of the model's output.

Core techniques in prompt engineering include zero-shot prompting (asking the model to perform a task without examples), few-shot prompting (providing examples of the desired output), and chain-of-thought prompting (instructing the model to explain its reasoning step-by-step). Advanced prompt engineering might involve structured templates, system prompts that define the model's persona, and retrieved context (as seen in RAG).

For AI trainers, prompt engineering is a foundational skill. Many evaluation tasks require trainers to write prompts from scratch to test specific model capabilities or to rewrite user prompts to make them clearer before evaluating the model's response. Red-teaming also relies heavily on adversarial prompt engineering to discover model vulnerabilities.

What this means for trainers

As an AI evaluator or trainer, strong prompt engineering skills allow you to probe the edges of a model's capabilities, write high-quality training demonstrations, and test for safety flaws effectively.