What AI coding tool should you use?
Find out which AI coding tool is best for your needs.
AI tools aren’t just changing how engineers work—they are enabling a whole new set of people to build apps, websites, and more. In the past few years, AI has gone from helping to write one-off scripts to a fullstack development suite that can create entire apps from a few prompts.
In this post, we’re diving into the three main buckets of AI coding tools:
General purpose AI chatbots
AI-Integrated IDE assistants
Web-based app generators
We’ll unpack their pros, cons, and real-world examples, and see how they’re driving the next wave of developer productivity.
Let’s dive in!
General-purpose AI chatbots
General-purpose AI chatbots are helpful go-to assistants if you’re looking to jumpstart an initial coding task or write a one-off script. Often these tools do many things, not just code generation, and therefore aren’t optimized for development.
However, these tools sit on some of the best AI APIs available, so are still great at code generation. They allow you to write simple prompts, upload documentation, or even provide existing scripts, and get back code snippets, functions, recommendations, or even small modules.
Example tools:
ChatGPT: Widely used for generating code snippets and debugging blocks of code.
Claude: Some developers may find it generates better code than ChatGPT.
Best for:
Generating standalone scripts.
Getting assistance with understanding existing code.
Developing small components.
Pros:
Speed & convenience: These tools can turn ideas into code within seconds, which means less time spent on boilerplate and more time solving unique problems.
Accessibility: For newcomers and veterans alike, these tools have the lowest barrier to entry, providing instant examples and reducing the need to scour documentation, learn new tools, or set up development environments.
Learning aid: When you’re stuck or want to understand best practices, asking a tool like ChatGPT can quickly break down complex logic into digestible steps.
Cons:
Accuracy issues: Despite their rapid output, AI chatbots aren’t foolproof. They sometimes produce code that doesn’t fully account for edge cases, contains subtle bugs, and has little to no context of the rest of your code or tech stack.
Simplicity: These tools are built to be generalized chat assistants, so they don’t come with all the bells and whistles of other AI development tools. For instance, they can’t generate code across multiple files at once, deploy your code to a server, or integrate with a backend solution.
These tools were the first consumer-friendly AI products that transformed the coding landscape. Many developers still use them as a helpful assistant in their workflow, but they have been left behind by more serious developers looking for more integrated solutions.
AI-integrated IDE assistants
Imagine having ChatGPT inside your IDE, watching your every keystroke, and offering suggestions as you type. AI-integrated IDE assistants like Cursor, GitHub Copilot, and Windsurf are designed to do just that. They’re not just about generating code—they’re about understanding context.
Example tools:
GitHub Copilot: The OG of AI-integrated IDE assistants, Copilot has become indispensable for many developers. Its recent evolution includes multi-file editing capabilities and context-rich completions, significantly boosting productivity.
Cursor: Known for its ability to understand code across multiple files through vectorized codebase analysis, Cursor provides more holistic suggestions and has been embraced by developers who value its “big picture” approach.
Windsurf: Although perhaps less known than Copilot and Cursor, Windsurf is fast becoming one of the leading AI-integrated IDEs because of its cleaner UI/UX and adeptness at working within large codebases.
Best for:
Engineers looking to bring AI into their development processes.
Teams of engineers working on larger code bases.
Non-developers who want the most control of their AI development process (and subsequently are interested in learning more traditional development practices).
Pros:
Context-awareness: These tools access your entire codebase to provide tailored suggestions. They know your variable names, your project’s architecture, and even your personal coding style.
Seamless integration: With plugins for popular IDEs (like Visual Studio Code), they blend into your existing workflow without disrupting your focus.
Efficiency boost: By offering real-time autocompletion, refactoring suggestions, and even error detection, they help you maintain a steady flow—even when juggling multiple files or projects.
Cons:
Learning curve: While they’re designed to be intuitive, integrating these assistants into your daily workflow sometimes requires an adjustment period—especially if you’re a non-developer!
Potential distractions: Constant suggestions and pop-ups might interrupt your train of thought if you’re not used to the constant nudges.
Quality variability: Not all suggestions are created equal—sometimes, the generated code may not align perfectly with your intended design, requiring extra tweaks.
For many, these integrated assistants are like having a pair of extra hands—ones that know the ins and outs of your project and help you keep your coding momentum going.
Web-based app generators
While AI chatbots and IDE assistants help with parts of the process, fullstack app generators take things a step further by assembling entire applications almost automatically. Tools like Bolt, Replit, and Lovable empower developers to generate complete app builds—including front-end, back-end, and sometimes even database configurations—with minimal input.
Example tools:
Bolt: Bolt was one of the first movers in this space. Its main advantages over the other tools are its ability to edit code directly in your web browser, slick Supabase and Netlify integrations, and most recently, mobile app development workflow.
Replit: Replit stands out for its full suite of native tools, including native database, hosting, mobile app, and more. You don’t need separate accounts for hosting or database support.
Lovable: A newer entry in the market, Lovable is gaining ground fast. Some of their differentiators include no-code UI editing, robust Supabase and GitHub integrations, and development velocity.
Best for:
Indie hackers that want to quickly ship web apps, mobile apps, landing pages, and more.
Product managers, designers, and engineers looking to create fully functional prototypes.
Hobbyists who want a low learning curve to start developing projects.
Pros:
Rapid prototyping: Fullstack generators allow you to go from idea to working application in minutes, which is a game changer for startups and experimental projects.
Consistency: By using standardized templates, these tools help maintain consistency in code structure and best practices across an application.
End-to-end solutions: They tackle both the front and back ends, which allows you to build fullstack apps in one integrated solution.
Cons:
Limited customization: While they excel at generating a functional prototype, the output might require significant reworking to meet the specific needs of a production environment.
Abstraction overhead: Understanding the generated codebase can be challenging if the tool abstracts too much, leaving you in the dark about underlying logic.
Quality trade-offs: Automated code is great for a first draft, but it often lacks the fine-tuned optimization that comes from hand-crafted code—manual refinement is almost always needed.
These fullstack generators are particularly exciting for teams looking to reduce time-to-market and experiment with innovative ideas without getting bogged down in repetitive setup tasks.
Final thoughts
No single AI tool will solve every developer’s problem—but by combining these three buckets, you get a powerful ecosystem that can cover everything from the initial spark of an idea to a fully functioning application.
General-purpose AI chatbots serve as your ideation engine, turning natural language into code drafts.
Web-based app generators let you prototype and even build entire applications at unprecedented speed.
Integrated IDE assistants keep you agile and informed as you write, refine, and debug your code in real-time.
The rise of AI coding tools is more than a productivity hack—it’s a paradigm shift in how we build software. While human ingenuity remains at the heart of great code, these AI companions free you up to focus on creativity, strategy, and the unique problems that only a human mind can solve.
The key is to leverage each tool for what it does best while remaining vigilant about quality and security. It’s an exciting time to code—whether you’re a beginner looking for guidance or a seasoned developer eager to accelerate your workflow.
This post was created by Garrett.