Writing Prompts

When working with LLMs to generate Python code for data analysis, your success depends a lot on how you ask. This process is called prompting, and the way you frame your request directly shapes the quality of the code you get back.

Prompting is about helping the model by removing ambiguity. If your request is vague, the AI may fill in the gaps in ways you did not intend. The clearer and more specific you are, the more likely you’ll receive useful, correct, and readable code.

📚 What’s Covered in This Section

We’ve organized prompting techniques into a comprehensive workflow:

🏗️ Building Better Prompts

The foundation of effective prompting—crafting clear, specific requests:

  • Being detailed, specific, and avoiding ambiguity
  • Specifying the libraries you need (Pandas, Matplotlib, etc.)
  • Decomposing complex problems into smaller, manageable steps

🎯 Guiding AI Behavior

Shaping the AI’s responses—setting context and controlling output style:

  • Using personas or roles to get expert-level responses
  • Adapting code complexity to match your skill level
  • Using inline comments or pseudocode to guide code structure
  • Including test cases to ensure correctness

🔄 Working with AI Responses

What to do after you receive code—refining and validating results:

  • Iterating and refining through conversation
  • Running and validating test cases
  • Checking that outputs make sense for your data

📊 Data Science Prompting Examples

Real-world examples for data analysis workflows:

  • AI-assisted learning and debugging techniques
  • Researching different analysis approaches
  • Complete workflow examples for common data science tasks
  • Pandas and Matplotlib-specific prompting patterns

💡 Prompting Tips & Tools

Practical strategies to make prompting more efficient and manageable:

  • Using system prompts for consistency across conversations
  • Data privacy considerations when working with sensitive data
  • LLM-specific prompting guides and best practices
  • Building your personal prompt library

🎯 The Key Principle

Effective prompting is about removing ambiguity and guiding the AI to produce code that matches your specific needs. Throughout these sections, you’ll learn techniques to consistently get Python code that’s accurate, readable, and aligned with your data analysis goals.

Tip

Don’t worry about applying every technique every time. Start with Building Better Prompts to master clarity and specificity, then gradually incorporate techniques from Guiding AI Behavior as you become more comfortable. Prompting is a skill that improves with practice.