Prompt Engineering
The Art of Context

Unlock the full potential of LLMs. Learn to guide, constrain, and optimize AI behavior through precise instructions.

The Vocabulary

Understanding the different modes of instructing an AI model.

Zero-Shot

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Ask Directly

Asking the model to perform a task without any examples.

Example: "Translate 'Hello' to French."

Few-Shot

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Learning by Example

Providing 1-3 examples before the actual request to guide style and format.

Significantly improves reliability.

Chain of Thought

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Step-by-Step

Asking the model to "think aloud" or break down steps before answering.

Crucial for math and logic puzzles.

System Prompt

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Setting the Persona

The initial instruction that defines behavior.

"You are a helpful coding assistant who explains bugs clearly."

Interactive: The Reasoning Process

See how different prompting styles lead to different outcomes. Click the buttons to simulate the model's path.

Question: "If I have 5 apples, eat 2, and buy 3 more, how many do I have?"
Select a prompt style above...

Prompting Matters

Standard prompts often rush to an answer. Chain of Thought forces the model to show its work, reducing errors.

Advanced Strategies

Less is More

Models are chatty. Constraints rein them in.

Negative Constraints

"Do NOT apologize. Do NOT say 'As an AI language model'. Do NOT include fluff."

Length Constraints

"Answer in exactly 3 sentences." or "Summarize in under 50 words."

Knowledge Check

Test your Prompt Engineering skills.

1. What is "Few-Shot" prompting?

2. Why is "Chain of Thought" useful?

3. What is the best way to get a JSON output?