Prompt Engineering
Learn how to write effective prompts for AI language models to get better, more accurate, and more useful results.
What is Prompt Engineering?
Prompt Engineering is the practice of designing and optimizing text inputs (prompts) to get the best possible outputs from AI language models like ChatGPT, Gemini, or Claude. As AI becomes more powerful, the ability to communicate effectively with it is an increasingly valuable skill.
Why Prompts Matter
AI language models are extremely sensitive to how questions are phrased. The same question asked two different ways can produce dramatically different results. Good prompt engineering dramatically improves quality, accuracy, and relevance of AI outputs.
Core Prompting Techniques
Zero-Shot Prompting
Ask the model to complete a task with no examples. Works well for straightforward tasks.
Translate this text to French: "Hello, how are you?"Few-Shot Prompting
Provide a few examples of the desired format to guide the model's output.
Positive review → Positive
Negative review → Negative
"This product is amazing!" →Chain-of-Thought (CoT) Prompting
Ask the model to think step by step. Dramatically improves reasoning on complex tasks.
Solve this problem step by step:
If a train travels 60 km/h for 2.5 hours, how far does it go?Role Prompting
Give the AI a persona or role to shape its style and expertise level.
You are an expert Python developer. Explain decorators to a beginner.Prompt Structure Best Practices
- Be specific — Vague prompts produce vague results.
- Define format — "Answer in bullet points", "in 3 sentences", "as a table".
- Set context — Tell the model who you are and what you need it for.
- Iterate — Refine your prompt based on what the model returns.
- Use delimiters — Use quotes or code blocks to clearly separate your input data from instructions.
What's Next?
Understand the underlying models with Artificial Intelligence, or see how AI learns from data in Machine Learning.