A practical look at how I use AI to build web and iOS apps, what works, what does not, and how to get real results

I have spent the past few years building apps across web and mobile, and lately I have been experimenting with AI coding tools to speed up my workflow. I have tried almost everything that is mainstream right now. ChatGPT, Gemini, Copilot, Claude Code in the CLI, and a few others. They all have strengths, but if I am being completely honest, Claude Code in the terminal has been the most impressive and the most consistent tool in my stack so far.

This post is not a comparison of all tools. It is a personal guide to what has worked for me, what has failed, and how I have learned to use AI to build products more efficiently. These are the lessons I picked up along the way, mixed with the mistakes I made and the tricks that actually moved the needle. Some people may disagree with me, and that is fine. Developers grow into their preferences and workflows over time. This is just mine.

Why AI coding will not replace developers

Before getting into the tools, I want to address a fear that keeps circulating in the tech world. The idea that AI will replace software developers entirely. My take is simple. AI enhances developers far more than it threatens them. A crude but useful analogy is this. A carpenter with only hand tools might build one chair in six hours. Give that same carpenter power tools and they might produce five chairs in the same amount of time. The tools do not make the carpenter irrelevant. They amplify capability and unlock potential that was not accessible before.

AI feels exactly like that to me. It is a power tool. Not a replacement for the person holding it.

What stood out

I have tried the usual lineup. ChatGPT is great for reasoning tasks and architectural planning. Gemini has improved a lot and is strong with research and documentation. Copilot is convenient in IDEs, but in my view it still has a long road ahead before it becomes something I would lean on heavily for serious application logic.

Claude Code, especially in the CLI, has been in a league of its own. I can give it a folder and it understands the structure, the relationships, the files, and the job I am trying to get done. The speed and accuracy it brings when working with real projects, not isolated code samples, is what convinced me to adopt it deeply into my workflow.

Garbage in, garbage out

AI is not magic. It reacts to the information you provide. If your prompt is vague, incomplete, or half thought out, the output will match that quality. The phrase I like to use is garbage in, garbage out. Think of AI as a strong intern. The intern works fast and gets a lot done, but the intern only knows what you tell them. They cannot read your mind. They do not understand the full context of your system. They will not automatically consider security flaws, edge cases, or performance problems unless you instruct them to.

This is why AI coding still requires real engineering judgment. You are the expert with the global context. It is your job to guide the intern.

How to prompt for better results

Precision is everything. Half of the frustration people have with AI coding tools comes from instructions that are too general. If you say something like:

Build me a login controller

You will get generic boilerplate. But if you say:

Build an iOS login controller in Swift that separates UI logic from networking. Include email and password validation, loading states, error handling, and a clean function that calls loginUser(email, password) from my existing API class.

The difference is dramatic. Good prompts save hours of cleanup. They produce structured, thoughtful code. And they reduce the amount of time you spend correcting the assistant.

Everything's experimental, commit a lot!

One of the most useful habits I built is committing after every helpful AI assisted change. Sometimes the model gets it right for five consecutive prompts and then suddenly takes a turn that breaks half your files. When that happens, having clean commits lets you revert with zero stress. Treat each AI suggestion like a mini experiment. Keep the good ones. Throw away the rest.

Documentation becomes effortless

Developers love building and hate writing documentation. AI flips this. It can write clear inline comments, detailed commit messages, and complete pull request descriptions without breaking a sweat. What took ten minutes of mental friction now takes five seconds. This alone justifies using AI tools even if you never let them touch your core logic.

AI and testing

AI is surprisingly good at generating unit tests. For web and iOS apps, this saves an enormous amount of time, especially when dealing with input validation, API error handling, or any scenario state that would normally require repetitive manual work.

The only caveat is that AI sometimes writes tests that do not fit your conventions or exposes private functions that should stay private. A quick review solves this. The time savings are still massive.

Resolving conflicts and debugging

Claude Code in particular has been incredibly accurate at resolving merge conflicts. The accuracy shocked me. It understands intent better than I expected and produces conflict resolutions that match what I would have done manually.

For debugging, pasting stack traces into AI tools has helped me narrow down obscure problems in minutes. When something crashes deep inside a library or framework, AI can reason through the possible causes faster than searching through threads or guessing manually.

Final thoughts

AI coding tools are not perfect, but they are already powerful enough to transform how developers work. They accelerate learning, unlock speed, and remove friction from everyday tasks. I do not believe they will replace programmers. I believe they will enable a new generation of builders who can do more than ever before with the same amount of time.

If you are a developer, you owe it to yourself to explore these tools now. Not out of fear, but out of opportunity. The sooner you learn to guide them, the sooner you will feel the lift in your own capability.

AI is the next major productivity boost in software development. Treat it like a power tool. Use it wisely. And build more than you ever thought possible.