What makes a good prompt when working with AI models?

Prompt engineering is the practice of designing inputs that elicit optimal outputs from language models. It's a critical skill for building effective AI applications.

Be specific and clear: Vague prompts get vague responses. Instead of "Summarize this," try "Provide a 3-sentence summary focusing on the key technical decisions and their trade-offs." Explicitly state the format you want.

Use structured prompts with sections: Background context, specific task, format requirements, and constraints. This helps the model understand what you need. Markdown formatting (headers, bullets) improves prompt readability for both humans and models.

Few-shot prompting provides examples of the input-output pattern you want. Show 2-3 examples of the task done correctly. This dramatically improves consistency and accuracy for specific formats or reasoning patterns.

Chain-of-thought prompting asks the model to reason step by step. Adding "Let's think through this step by step" or "Show your reasoning" improves performance on complex reasoning tasks.

Role assignment ("You are an expert software architect...") sets context and expertise level. System prompts in chat APIs are ideal for persistent role and behavior instructions.

Iterate and test systematically. Small wording changes can significantly impact outputs. Build prompt evaluation datasets to measure improvements objectively.

For production: version control your prompts, implement prompt templates with variables, handle edge cases, and consider prompt injection security. Understanding these patterns is essential for building reliable AI features.