5 LLM Prompting Techniques Every Developer Should Know

Getting useful results from AI tools like ChatGPT, Claude, or Gemini can be tricky at first. The difference between a helpful response and a generic one often comes down to how you ask the question.

Many developers start by typing something like "help me with Python" and wonder why the answer doesn't fit their specific needs. The key is learning how to communicate with these tools more effectively.

Here are five techniques that can help you get better, more targeted responses from any LLM, whether you're working on your first project or your hundredth.

1. Assign specific roles

One of the simplest ways to improve your results is telling the AI what perspective to take. Instead of asking "How do I use React hooks?", you could try: "Act as a React instructor explaining useState and useEffect to someone new to hooks."

This approach works because it gives the AI context about the tone and depth you're looking for.

A general question might get you advanced concepts when you need basics, or overly simple explanations when you want detailed technical information.

You can also combine roles for more specific results. For example:

"You're a senior developer doing a code review. Point out any issues with this JavaScript function and suggest improvements."

The role-playing technique helps ensure the response matches what you actually need rather than what the AI thinks might be generally useful.

2. Use step-by-step building

Complex requests can lead to overwhelming or unfocused responses. Breaking things down into smaller parts often works better.

Instead of asking for a complete web application, you might start with:

"Show me how to set up a basic Express server with one test endpoint." Then follow up with: "Now add user authentication to this server." And finally: "Add database integration for storing user data."

This approach has several benefits. You can verify each piece works before moving on.

You can adjust the direction if something doesn't fit your needs.

And you avoid getting stuck with a large chunk of code that's hard to understand or debug.

It's similar to how you might work with a teammate - you'd discuss the approach, implement pieces gradually, and review as you go.

3. Provide format examples

When you need output in a specific style or structure, showing an example is often more effective than describing what you want.

4. Set clear constraints

AI tools often provide more complexity than needed. Setting specific limits can help you get focused, practical solutions.

Instead of: "Write a Python script for data analysis."

Try: "Write a Python script for data analysis. Use only pandas and matplotlib. Keep it under 30 lines. Focus on reading CSV files and creating basic charts."

Constraints help in several ways:

• You get solutions that fit your actual environment and requirements

• The code is more likely to match your skill level and project needs

• You avoid unnecessary dependencies or overly complex approaches

This is especially helpful when you're learning something new and don't want to get distracted by advanced features, or when you're working with specific limitations like certain libraries or resource constraints.

5. Request code reviews

AI tools can be surprisingly good at finding issues in their own output if you ask them to take a second look.

After getting a solution, you can follow up with: "Review this code for potential problems, edge cases, and improvements."

This second-pass approach often reveals:

• Security issues that weren't obvious initially

• Performance concerns

• Missing error handling

• Better ways to structure the solution

It's like getting a fresh perspective on the code. Sometimes the AI will suggest completely different approaches that work better for your specific situation.

You can also ask it to explain the code back to you, which helps ensure you understand what's happening and can modify it if needed.

Combining these techniques

These approaches work well together and can be mixed based on what you're trying to accomplish.

For quick scripts or simple problems, setting constraints might be enough. For learning complex topics, role assignment combined with step-by-step building often works well. When working on larger projects, examples plus constraints can help maintain consistency.

The key is being intentional about how you structure your requests rather than hoping the AI will guess what you need.

Getting better results

A few general principles can help improve your interactions with AI tools:

Be specific about your context: Mention your programming language, framework, or any constraints you're working with.

Don't be afraid to iterate: If the first response isn't quite right, you can ask for adjustments or clarifications.

Provide relevant details: The more context you give about your project and requirements, the more targeted the response will be.

Ask follow-up questions: AI tools can explain their reasoning, suggest alternatives, or dive deeper into specific parts of a solution.

Why this approach works?

These techniques essentially treat AI tools as collaborative partners rather than search engines. Instead of hoping to get lucky with a single query, you're having a structured conversation that builds toward the solution you actually need.

This approach tends to be more efficient than repeatedly asking new questions from scratch.

It also helps you learn more effectively because you can ask for explanations, request modifications, and understand the reasoning behind different approaches.

The goal isn't to find the perfect prompt on the first try, but to develop a communication style that consistently gets you useful, relevant results.

Getting started

You don't need to master all these techniques at once. Pick one that seems most relevant to your current work and try it out.

If you're learning something new, start with role assignment to get explanations at the right level. If you're building something complex, try the step-by-step approach.

If you need specific formatting, use examples.

As you get comfortable with one technique, you can start combining them or trying others. The key is developing habits that make your AI interactions more productive and targeted to your actual needs.

These tools can be genuinely helpful for development work, but they work best when you know how to communicate with them effectively.

Learning to structure your requests well is a skill that pays off across different AI platforms and different types of problems.

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