What is Prompt Engineering?

AI & ML

Prompt Engineering

The practice of crafting and optimizing input prompts to guide LLMs toward producing desired outputs, including techniques like few-shot learning and chain-of-thought.

Prompt engineering is the art and science of designing effective prompts to get the best results from large language models. A well-crafted prompt can dramatically improve output quality, accuracy, and consistency.

Key techniques include zero-shot prompting (directly asking without examples), few-shot prompting (providing examples of desired input-output pairs), chain-of-thought (asking the model to reason step by step), and role prompting (assigning a persona to the model).

Advanced techniques include tree-of-thought (exploring multiple reasoning paths), self-consistency (generating multiple answers and selecting the most common), and retrieval-augmented prompting (injecting relevant context from external sources).

System prompts define the model's behavior, personality, and constraints. They're essential for building consistent AI applications. Best practices include being specific about output format, providing constraints, and iterating based on failure cases.