Effective Prompting

Effective Prompting for LLMs

Prompting is the art and science of communicating with large language models. The quality of your prompts directly affects the quality of the model’s responses. Learning to write effective prompts is one of the most valuable skills for working with LLMs.

What is a Prompt?

A prompt is the input you provide to an LLM. It can be:

• A question: “What is photosynthesis?”
• An instruction: “Translate this text to Spanish”
• A request: “Write a poem about autumn”
• Context + question: “Given this data… what trends do you see?”

Why Prompting Matters

The same model can produce vastly different outputs depending on how you prompt it:

Poor Prompt:

write code

Better Prompt:

Write a Python function that takes a list of numbers and returns the median value.
Include error handling for empty lists and non-numeric values.
Add docstring and type hints.

The second prompt produces dramatically better code because it’s specific, clear, and provides context.

What You’ll Learn

This section covers:

The Prompting Mindset

When working with LLMs, think of them as:

• Extremely literal: They do exactly what you ask, not what you mean
• Context-dependent: They use only the information you provide
• Pattern matchers: They generate text based on patterns learned from training data
• Non-deterministic (usually): Same prompt can yield different responses

Quick Prompting Tips

Before diving deeper, here are five essential tips:

1. Be Specific: Vague prompts get vague responses
2. Provide Context: Give the model background information it needs
3. Show Examples: Demonstrate the format or style you want
4. Iterate: Refine your prompts based on the results
5. Control Parameters: Use temperature and other settings strategically

Ready to Start?

Let’s begin with the fundamentals: