🧠 Prompt Engineering Interactive Lab

This interactive lab demonstrates how different prompt engineering techniques can dramatically affect AI outputs.

Experiment with various techniques and see how the same query produces different results based on how you frame your prompt. This is a hands-on companion to the blog post "What is Prompt Engineering?"
Select Model
Prompt Technique
0.1 1

Example Prompting Scenarios
Examples

Understanding Prompt Engineering

Prompt engineering is the practice of crafting inputs to AI systems to elicit desired outputs. It's a key skill for effectively using large language models.

Why Prompt Engineering Matters

The same model can produce dramatically different results based solely on how you frame your prompt. This demo lets you experience this firsthand by comparing different techniques:

  • Basic Prompting: Direct questions yield direct answers, but may lack depth or context
  • Role-Based Prompting: Giving the AI a persona or expertise lens changes its perspective
  • Step-by-Step Reasoning: Requesting explicit reasoning steps improves accuracy for complex tasks
  • Chain of Thought: Extended reasoning that connects concepts leads to more comprehensive answers
  • Few-Shot Learning: Showing examples of desired outputs helps the model understand your expectations

Experiment Tips

  • Try the same query with different techniques to see how responses vary
  • Adjust the temperature to see how it affects output creativity vs. precision
  • For complex questions, compare basic prompting with reasoning-based techniques
  • For domain-specific questions, try role-based prompting with relevant expertise

This demo uses the Google Gemma model family via OpenRouter's API.

Setup Information

This demo uses the OpenRouter API to access Gemma models. The default API key has limited quota.

For unlimited use:

  1. Sign up at OpenRouter
  2. Get your API key from the dashboard
  3. Create a .env file in this directory with: OPENROUTER_API_KEY=your_api_key_here