Ultimate Guide: 10 Tips for Maximizing Your AI Coding Tools

AI coding tools can save you time, improve code quality, and make development easier. But to get the most out of them, you need to use them effectively. 

Here's a quick summary of how to maximize your AI coding tools:

• Choose the right tool: Look for features like code generation, IDE integration, and security scanning.
• Write better prompts: Be clear and specific to get accurate suggestions.
• Provide context: Share existing code, file structures, and coding styles for better results.
• Set preferences: Configure language options, frameworks, and code styles to match your workflow.
• Improve code reviews: Use AI to detect bugs, optimize performance, and ensure consistency.
• Speed up testing: Automate unit tests and fill coverage gaps.
• Keep learning: Stay updated on new features and experiment with improvements.

Quick Comparison Table 

Next steps: Start small by using AI tools for repetitive tasks, refine your prompts, and gradually expand their role in your workflow. Always review AI-generated code to ensure quality and security.

AI Coding 101: Ultimate Prompt Guide (37 tips)

1. Pick the Best AI Coding Tool

Choosing the right AI coding tool can significantly improve your workflow. Here's what to consider.

Compare Core Features

When assessing AI coding tools, pay attention to these key features:

Pricing matters too: GitHub Copilot costs $10/month for individuals and $19/user/month for businesses. Amazon CodeWhisperer offers a free tier for individuals and charges $19/user/month for professionals [1].

Learn Tool Basics

Once you've chosen a tool, get familiar with it using these tips:

• Dive into the documentation: Understanding the core features is essential. For example, Cursor's manual indexing can provide more accurate assistance [2].
• Keep key files open: Open relevant files in your IDE to give the tool better context and improve its suggestions [2].
• Customize to your environment: Use internal libraries and APIs to adapt enterprise tools like Amazon Q Developer to your specific needs [2].
• Enable security features early: Turn on built-in security scanning (like in Amazon CodeWhisperer) to identify vulnerabilities from the start [1].

Once you're comfortable with the basics, focus on crafting better AI prompts to get the most out of your tool.

2. Write Better AI Prompts

Once you're familiar with the basics of the tool, the next step is crafting prompts that get better AI-generated suggestions. This section explains how to structure and refine your prompts for the best results.

Structure Your Prompts

The way you structure your prompts plays a big role in the quality of the suggestions you receive. A good prompt is clear, detailed, and provides the right context.

“AI tools thrive on clarity and context. When formulating a prompt, provide enough information to avoid ambiguity.” - Iri Denis, Web3 developer and investor [3]

Here are a few tips for structuring prompts effectively:

• Start broad, then narrow down: Begin with a general goal and gradually add specific details.
• Specify the output format: Clearly indicate if you need code, documentation, or explanations.
• Set boundaries: Include any preferences or limitations, such as coding style or specific frameworks.

Once you've created a prompt, you can refine it further through testing and iteration.

Test and Improve Prompts

Improving prompts is an ongoing process. Testing and refining them can lead to more accurate and useful results.

Example:

• Initial: "Generate a Node.js API endpoint"
• Refined: "Generate a Node.js API endpoint using Express to handle GET requests for fetching user data from a MongoDB database" [3]

“The AI is often very good at improving upon its initial attempt when asked to try again. Once you’ve got something that works, you can revise further to improve the code’s performance or maintainability.” - Alex M, AI Powered Dev Tools [4]

Steps to optimize your prompts:

• Test initial responses: Start with a basic prompt and evaluate the output.
• Add context as needed: Include specific details, such as error messages or edge cases, to refine the results.
• Reference existing code: Open relevant files in your IDE to give the AI a better understanding of your codebase.

3. Provide Better Context

Giving clear context is key to getting accurate and relevant code suggestions. The quality of AI-generated code depends heavily on how well you communicate your project's details and requirements.

Add Key Code Examples

Sharing specific code examples - like existing functions, file structures, or your preferred coding style - helps the AI better understand your project.

“Refer to your pre-existing code as much as possible. Even if you’re starting on a completely new feature, it helps to give the context of the environment you are coding in and your preferred coding style.” - Alex M [4]

A detailed context sets the foundation for precise and helpful instructions.

Write Clear Instructions

Once you’ve provided context, focus on crafting clear instructions to guide the AI effectively. Well-structured instructions lead to better results.

• Start with the goal: Clearly state what you want to accomplish. For example, instead of saying "create an API", specify: "Create a REST API endpoint using Express.js that handles user authentication with JWT tokens."
• Include technical requirements: Outline any specific constraints or preferences that the AI should follow.
• Use industry-standard terminology: Stick to widely-recognized programming terms to ensure alignment with the AI's knowledge base.

“The ideal prompt contains all the information the AI needs to complete the task and nothing else.” - Alex M [4]

A real-world example from DEV Community member Iri Denis highlights this approach. In December 2024, they improved their prompt from "Generate a Node.js API endpoint" to "Generate a Node.js API endpoint using Express to handle GET requests for fetching user data from a MongoDB database." The added details led to more accurate and helpful code suggestions [3].

4. Set Up Your Preferences

Adjust your AI tool's settings to fit your development environment and coding needs.

Set Language Options

Language settings play a big role in the quality of AI-generated code. Tools often perform best with languages like Python and JavaScript, so make sure these preferences are set correctly.

If you're working with less common languages or frameworks, include additional details about your setup. This helps the tool generate more accurate code, especially when using niche libraries that might not be well-represented in its training data.

Once your language settings are dialed in, turn your attention to selecting features that align with your workflow.

Choose Tool Features

The right features can make a big difference in how smoothly your development process runs. Enable the ones that fit your needs and coding habits.

• Code Completion Settings
  Start with conservative code completions and tweak them as necessary.
• Documentation Generation
  Turn on automatic documentation and set your preferred comment style to keep things consistent.
• Code Review Assistance
  Use code review tools and fine-tune notifications to keep feedback manageable without disrupting your workflow.

To get the best results, provide clear instructions and highlight what the tool should avoid. The more specific you are, the better the code quality will be.

5. Generate Better Documentation

Clear and thorough documentation is key to making the most of your AI tools. By combining well-defined prompts with context, you can create documentation that scales projects efficiently while ensuring consistency.

Create Code Comments

AI can help you document not just what the code does, but also why it does it.

Start with general requests for comments, then refine them to ensure accuracy and consistency. Beyond inline comments, focus on creating comprehensive summaries of your code.

Write Code Overviews

Providing enough context allows AI to generate documentation that captures the bigger picture of your code's role and impact.

• Start with Purpose Documentation: Use AI to draft an overview of your code's purpose, then refine it with relevant business context.
• Add Technical Details: Include technical specs, dependencies, and integration points to ensure the documentation highlights critical connections in your code.
• Maintain Documentation Standards: Configure your AI tool to align with your team’s documentation practices by following these tips:

“AI tools thrive on clarity and context. When formulating a prompt, provide enough information to avoid ambiguity.” - Iri Denis, Web3 developer and investor [3]

6. Improve Code Reviews

AI is changing the way code reviews are done, offering the ability to analyze thousands of lines of code in seconds while delivering consistent, objective feedback.

Find Code Problems

AI tools are highly effective at spotting potential issues before they reach production. Using machine learning and natural language processing, they can identify bugs, security risks, and inefficiencies that human reviewers might miss [6].

Catching problems early saves money - fixing them during development can be up to 100 times cheaper than addressing them in production [6]. Beyond detection, these tools often suggest actionable fixes to help developers resolve issues quickly.

Get Code Suggestions

AI doesn't just point out problems - it offers solutions. For example, the Linux Foundation's use of CodeRabbit revealed critical gaps between documentation and test coverage, leading to noticeable improvements in code quality [8].

Here’s how to get the most out of AI-generated suggestions:

• Set Custom Rules: Tailor coding standards to match your team’s needs [7].
• Evaluate Recommendations: Ensure the suggestions align with your project’s goals [6].
• Create Feedback Loops: Track how well the suggestions work and refine configurations over time.

“CodeRabbit has immediately reduced the amount of time our code changes spend in review. This significant enhancement has increased our velocity and improved our ability to deliver new, bug-free features to our customers.”
– Blake Gardner, Chief Technology Officer, CrowdHealth [8]

The benefits are clear. Axolo users saw pull request merge times drop from 2.40 days to 1.51 days within two weeks of using AI. Similarly, Bito AI reports cutting manual review efforts by 87% [7]. That said, pairing AI insights with human judgment ensures the best results, as humans can account for context that AI might overlook [6].

7. Speed Up Testing

AI tools can reduce unit testing time drastically, shrinking the process from hours to mere minutes [10]. After improving code reviews, it's time to streamline your testing workflow.

Create Unit Tests

AI tools can analyze your code's structure and behavior to generate unit tests automatically [9].

For example, Keploy can achieve 90% test coverage in just minutes [10]. Simply run the AI extension to generate initial tests, then review and tweak them as needed.

Find Testing Gaps

AI tools can pinpoint gaps in test coverage by analyzing test history and code complexity [11]. Key features include:

• Real-time Analysis: AI monitors code changes and flags untested areas instantly [10].
• Predictive Analytics: It predicts potential defects based on code complexity and past failures [11].
• Dynamic Updates: Test cases automatically adjust when features change [12].

These tools can seamlessly integrate with your current development workflow. When UI or API updates happen, the AI detects and modifies test scripts automatically [11]. This reduces maintenance time and ensures your test coverage stays consistent.

8. Keep Learning

AI coding tools are always changing and improving [13]. To get the most out of them, it's important to stay informed and try out new features.

Track New Updates

Keeping up with updates helps you maintain efficiency. Different types of updates can affect your workflow in various ways:

Check official documentation and release notes to understand the changes and how they can help you. For instance, a major update might improve code generation or add support for new programming languages, making your workflow smoother and faster.

Once you know what’s new, test these updates in a controlled environment to see how they work for your projects.

Try New Features

When testing new AI features, follow a clear plan:

• Set up a separate test branch to evaluate the new tools.
• Track how the feature performs by noting success rates, time saved, code quality, and any challenges.
• Share your findings with your team to help everyone improve.

While experimenting, focus on these key areas:

• Test different prompts to see what gets the best results.
• Build a collection of effective prompts for repeated tasks.
• Adjust AI-generated code to fit your specific needs.
• Share your discoveries with your team to speed up collective learning.

Conclusion: Next Steps

Use these strategies to get the most out of your AI coding tools. Think of your AI tools as junior assistants - they're helpful but still need your guidance.

Here’s a practical plan for integrating AI into your development process while keeping quality and security in check:

This plan offers a structured approach to safely and effectively incorporating AI into your coding workflow.

“AI coding tools can boost your productivity and free you up to focus on the creative parts of development - but they’re just tools. The real magic happens when you combine their power with your skills and judgment as a developer.” - Kudzai Murimi, FullStack Developer and Content Writer [5]

Keep a balance between AI assistance and thorough human oversight. Start with low-risk tasks to build familiarity, then gradually expand usage while prioritizing security and quality.

Detailed prompts and clear context lead to better AI outputs. Similarly, a well-thought-out workflow improves overall code quality. Build a prompt library for repetitive tasks and always manually review critical code to ensure it meets your project’s standards.ools