Prompt Engineering

Prompt engineering is the art and science of crafting inputs to get the best outputs from Large Language Models. It's become a critical skill in the age of GPT, Claude, and other LLMs.

Key Principle: Clear, specific prompts with context and examples yield better results than vague requests.

Core Techniques

1. Zero-Shot Prompting

Direct instruction without examples.

"Translate this to French: Hello, how are you?"

2. Few-Shot Prompting

Provide examples to guide the model.

"Positive: Great product!
Negative: Terrible service.
Classify: The food was amazing!"

3. Chain-of-Thought (CoT)

Ask the model to explain its reasoning step-by-step.

"Let's think step by step: What is 15% of 80?"

Prompt Template Example

A well-structured prompt template for consistent results.

python

Output:

Click "Run Code" to see output

Best Practices

Be Specific: "Write a 200-word blog intro" vs "Write something"
Set Context: Define role, audience, and constraints
Use Delimiters: Triple quotes, XML tags, or markdown to separate sections
Iterate: Refine prompts based on outputs
Temperature Control: Lower (0.2) for factual, higher (0.8) for creative