Welcome to prompt best practices—a guide to writing effective prompts for large language models (LLMs). Whether you’re generating content, solving problems, or building applications, following a few key principles can help you get better, more reliable results. Let’s dive in.

1. Define Your Objective Clearly: Start by clearly stating your goal. What do you want the model to produce—an answer, a summary, an opinion, a creative take? Use direct, concise language to help the model focus. The more specific you are, the more aligned the output will be with your intent.
2. Provide Sufficient Context: LLMs are powerful, but they’re not mind readers. Set the scene with enough background so the model can interpret your prompt correctly. If there’s room for misinterpretation, adding context or a brief explanation helps steer the output in the right direction.
3. Structure Instructions with Clarity: If your task has multiple parts or is complex, break it down into smaller, clear instructions. Step-by-step formats often yield better responses than open-ended ones. Focus only on what’s necessary—excess information can confuse the model.
4. Specify the Desired Format: If you want a specific output structure (like a list, table, JSON, or paragraph), say so in your prompt. Placeholders can be especially helpful if you’re expecting a fill-in-the-blank response.
5. Iterate with Refinements: Prompts aren’t one-and-done. If a response isn’t quite right, refine the wording, simplify your instructions, or clarify your context. Slight changes can dramatically improve the outcome.
6. Control Output Length: To avoid responses that are too brief or too long, set clear expectations. For example, you can specify “limit to 3 bullet points” or “keep it under 100 words.” This helps you control verbosity and relevance.
7. Leverage Examples: Including examples helps guide the model’s style, tone, and accuracy. Positive examples show what you’re aiming for; negative examples can help steer the model away from unwanted outputs.
8. Adapt to Model-Specific Nuances: Different models behave differently. Some are better at creative writing, others at summarization or reasoning. Tailor your prompt to suit the model’s strengths. Also, take advantage of any advanced settings (like temperature, max tokens, or system prompts).

Writing great prompts is both art and science. With clear goals, structured input, and iteration, you can consistently produce better results—whether you’re working on content, code, or something entirely new.

Let’s apply these best practices to a real-world example: writing a blog post about the benefits of meditation. Putting it all together, your final prompt might look like this:

This final prompt applies all eight best practices to guide the LLM toward a well-structured, useful response that fits your content goals.

Great prompts are the foundation of reliable LLM applications. At Arato, we make it easy to structure, test, and iterate on your prompts—so you can build GenAI features that are consistent, trustworthy, and production-ready.