🧠 What Is AI Prompt Engineering?
Prompt engineering is the practice of crafting inputs—called prompts—to guide AI models like ChatGPT, Claude, or Gemini to produce accurate, relevant, and useful outputs. It’s essentially about how you ask the AI to get the best possible results [1].
🔑 Key Concepts & Terminology
1. Prompt
The input or instruction given to an AI model. It can be a question, command, or context-setting statement.
2. Context
Background information or framing that helps the AI understand the task better. Example: “You are a travel expert. Recommend a 5-day itinerary for Paris.”
3. Clarity & Specificity
Clear and specific prompts yield better results. Vague prompts often lead to generic or inaccurate responses.
4. Few-shot / Zero-shot / Chain-of-thought Prompting
- Zero-shot: No examples provided; the AI must infer the task.
- Few-shot: A few examples are given to guide the AI.
- Chain-of-thought: Prompts that encourage step-by-step reasoning.
5. System Instructions
Used in some platforms to set the AI’s behavior, tone, or role (e.g., “You are a helpful assistant”).
📌 Why It Matters
Effective prompt engineering:
- Boosts accuracy and relevance
- Enhances creativity and productivity
- Reduces bias and errors
- Enables automation of tasks like writing, coding, summarizing, and more [2]
🛠️ Tools & Resources
🔧 Tools
- OpenAI Playground – Experiment with prompt formats and model settings
- PromptHero – Browse and share prompt templates
- FlowGPT – Community-driven prompt library
- LangChain / LlamaIndex – Frameworks for building prompt-driven AI apps
- Code Interpreter / Notebooks – For testing and validating outputs
📚 Learning Resources
Coursera: Generative AI Prompt Engineering [3]
Hélène Voyer 