1. What is Prompt Engineering?
Prompt engineering is the skill of designing input prompts to guide AI models (especially LLMs) to produce accurate, useful, and creative outputs. The better the prompt, the more precise and reliable the AI's response.
2. Key Techniques
- Clarity: Use precise and unambiguous language.
- Context: Provide relevant background information in the prompt.
- Step-by-step: Ask the model to reason or explain steps.
- Constraints: Limit output style, length, or format.
- Examples: Include sample input-output for guidance.
3. Practical Examples
Basic Prompt: "Explain AI in simple terms."
Improved Prompt: "Explain AI to a 12-year-old using a real-world analogy in 3 short sentences."
Outcome: AI produces concise, understandable, and engaging response.
Improved Prompt: "Explain AI to a 12-year-old using a real-world analogy in 3 short sentences."
Outcome: AI produces concise, understandable, and engaging response.
Task-Oriented Prompt: "Generate a Python function to calculate Fibonacci numbers."
With Instructions: "Write a Python function called fibonacci(n) that returns a list of the first n Fibonacci numbers and add comments explaining each step."
With Instructions: "Write a Python function called fibonacci(n) that returns a list of the first n Fibonacci numbers and add comments explaining each step."
4. Tools for Prompt Engineering
- OpenAI Playground – test and refine prompts interactively
- LangChain – chain prompts for complex workflows
- Prompt templates – reusable prompt patterns for LLM tasks
5. Exercise
Choose a topic of your interest and write two prompts: one basic and one improved. Compare the outputs and identify how context, clarity, or constraints changed the result.
6. Inspirational Quote
"The right words can unlock the intelligence within machines." — AI Free Learning