prompt-engineering

To write effective LLM prompts, you need a structured framework that defines roles, tasks, constraints, and examples. This approach ensures prompts are clear, effective, and aligned with user intent to improve model performance.

The best way to format LLM outputs is by explicitly specifying concrete output formatting rules within your prompt. This practice ensures consistency and usability, preventing unpredictable model responses and aligning outputs with your downstream requirements.

You use few-shot examples by providing diverse, structured examples directly within your prompt. This guides the language model's behavior, improving performance and ensuring the model interaction generates responses matching your desired format and intent.

Modern prompt engineering best practices involve using structured guidelines, adding few-shot examples, and specifying output formats. This helps avoid outdated advice and common pitfalls when working with modern LLMs.

Yes, you can use structured guidelines to summarize a technical article by defining the role, task, and constraints in your prompt. This framework helps the LLM generate clear and effective summaries aligned with your intent.