Prompt engineering is the practice of crafting clear, precise, and effective inputs (called prompts) to get the best possible output from a Large Language Model (LLM) like ChatGPT.

It’s a bit like asking the right question in the right way — and in the world of AI, how you ask matters just as much as what you ask.

Why Prompts Matter

LLMs don’t “think” like humans. They predict the next word based on patterns they’ve learned. That means vague or poorly written prompts can lead to incomplete, inaccurate, or unexpected results.

For example:

• 🟠 Prompt: “Write about Paris.” → Too vague.
• 🟢 Better Prompt: “Write a 100-word travel guide for Paris focusing on art and architecture.”

Well-written prompts help guide the AI to:

• Focus on relevant content
• Understand your goal
• Match your tone or format

Prompt Methods: How to Get Better Results

There’s no one-size-fits-all prompt — but several proven methods can help you get clearer, more accurate, and more useful responses from an AI model. Here are a few you can start using right away:

1. Zero-shot prompting

Ask the AI a question or give a task without any examples.
👉 Example: “Summarize this article in 3 bullet points.”
Best for: Simple tasks or quick answers.

2. Few-shot prompting

Provide one or more examples so the AI learns the pattern.
👉 Example:
“Translate these to formal French:
– Hi, how are you? → Bonjour, comment allez-vous ?
– Thanks! → Merci !
Now translate: See you later!”

Best for: Tasks needing specific tone, structure, or logic.

3. Chain-of-thought prompting

Ask the AI to explain step-by-step reasoning before answering.
👉 Example: “Explain step by step how to convert Celsius to Fahrenheit.”
Best for: Complex questions, calculations, or logic-based tasks.

4. Role prompting

Tell the AI to “act as” a specific expert or persona.
👉 Example: “You’re an academic writing coach. Help me improve this abstract.”
Best for: Getting responses tailored to a specific voice or domain.

By combining these methods — or even layering them — you can steer the AI more effectively and get results that feel more natural, accurate, and helpful.

Prompt engineering bridges the gap between human intention and AI output. Whether you’re writing, translating, or summarizing, better prompts lead to better results — and using AI responsibly starts with clear communication.

✍️ Want to know what’s really happening behind the scenes when you write a prompt?
To understand how LLMs process your input — and why it matters for cost, context, and performance — let’s talk about tokens.

👉 Next article: What are Tokens and Why Do They Matter?