The world of software development is in constant flux, but few forces have driven as profound a shift as artificial intelligence. What once seemed like science fiction is now an everyday reality, with AI tools seamlessly integrating into developer workflows, promising not just incremental gains but a fundamental redefinition of productivity. In 2025, developers are finding themselves empowered by intelligent assistants, automated guardians of code quality, and even AI “colleagues” capable of tackling complex engineering tasks.
I have written a bunch of articles in CODE Magazine about AI. All of them have focused on learning AI, such as image generation, creating a local chat bot, and more. But what if you're not an AI developer? Maybe you're a ReactJS developer writing front-end code all day long. Or maybe you write REST APIs using Python all day long.
Let's be honest, AI is exciting, but many of us are still working day-in-day-out delivering business functionality code, things your employer needs today. Should you ignore AI? Far from it. This article explores how AI is boosting developer productivity across the software development lifecycle, complete with practical examples to illustrate its transformative power.
The AI-Powered Developer: A New Paradigm
As of today, at its core, AI for developers isn't about replacing human creativity; it's about augmenting it. By offloading repetitive, time-consuming, and error-prone tasks, AI frees developers to focus on higher-level problem-solving, architectural design, and innovative solutions. This shift fosters a more engaging and less frustrating development experience.
Let's dive into the key areas where AI is making a tangible difference.
There are many ways I see that AI can help you as a developer. This is by no means an exhaustive list, if you have ideas do share.
AI as Your Pair Programmer
The most visible and widely adopted application of AI in development is in code generation and intelligent completion. There are many competing tools you can use. Tools like GitHub Copilot, Tabnine, and Amazon CodeWhisperer act as highly intelligent pair programmers, anticipating your next move and suggesting relevant code snippets, entire functions, or even boilerplate structures. In fact, there are VSCode extensions that let you plug into any AI model to get specific help for your scenario. You can even use Ollama to run things locally if you're in an air-gapped secure environment. Of course, the capabilities of cloud-based models are far ahead of what Ollama on your local machine can do, but Ollama with a local model is still superpowers that you didn't know you had.
There are many benefits of incorporating AI as your pair programmer.
The first is, of course, speed. Using AI drastically reduces the time spent on writing repetitive code or searching for syntax. How often do you find yourself struggling to find the right syntax for a particular thing you're trying to do? Or writing repetitive code that you know you can write, but would rather have a helper write for you, and maybe even write it better than you? Like, find username out of a jwt token. I know how to do this, I just wish I didn't have to do this in every project I land in. Yes you decode the token, which means first convert base 64 to JSON, oh wait, first separate the three parts of the token, validate the signature, blah blah! Dear AI: Just do this for me, please?
The other obvious advantage is accuracy. Using AI minimizes typos and common syntax errors, leading to fewer debugging cycles. When I was a programmer in my teens, I took great pride in my accuracy and typing capabilities. I could type at > 140WPM without errors. Alas, as time has passed, my fingers have too grown older. I do make mistakes now. Unfortunate mistakes that take forever to find the errors they introduce. All because of a stupid typo. If I can have Microsoft Word correct my spelling mistakes, wouldn't it be nice if AI can fix the errors my IDE cannot catch?
And finally, like any good pair programmer, I learn from my AI buddy. See I've never been a fan of pair programming. I know I know, you can put those daggers back in their sheaths. But I learn differently from others. When I'm deep into programming, I don't want another person interrupting my thought process, or constantly interrupting asking questions. Pair programming may be great for the new person on the team, but as an experienced programmer (sorry for putting myself on a pedestal), I found pair programming was a lot of giveth and not enough taketh. I want to pair program with someone better than me, and those can be hard to find. AI frequently is better than me. Again, please don't see this as me putting myself on a pedestal, but good developers are very hard to find, and when I do get a chance to learn from one, I take that chance every single time. It's just that those chances are a few and far in between.
All right, enough talk. Let's see an example. I went ahead and installed Ollama and pulled down the Code Llama model. If this sounds Greek to you, please see this article where I talk about AI with no internet connection. I talk about how to use Ollama to create a local chatbot backed by a PDF. Although that article talked about an LLM indexing your content, you can use the same mechanism to work with code, except that you use a different model. There are many models to pick from, I picked Code Llama because it works well with code.
To allow the continue extension to talk my local model, I created a config.yaml, as can be seen in Figure 1, in my local profile in the .continue folder.
With this, you're ready to roll. On the left-hand side, you should see an icon for the continue extension. If you click on it, a chat window comes up, as can be seen in Figure 2.
Now, you can work with any code base, but I'll just pick the code I showed in my AI with no internet connection article. Some of the concepts were a bit wiry, so I open the index.py from that code example, and, with my mouse, select an arbitrary portion of the code, as can be seen in Figure 3.
I see that a couple of interesting tooltips have shown up. I can see that I could use “chat” by pressing CMD_L so I did. And I asked, “What does this code do?” and what happened next blew me away.
Here is the output it gave me.
AI Response:
This code defines a chain of functions that will be called in
sequence to retrieve information from the user. The chain
consists of three functions: itemgetter, retriever, prompt,
model, and parser. Each function takes an input and returns
an output. The output of one function becomes the input for
the next function in the chain.
Here's a breakdown of what each function does:
itemgetter: This function retrieves a specific item from an
object, such as a dictionary or list. In this case, it
retrieves the "question" key from the input object.
retriever: This function is responsible for retrieving
the user's response to a given question. It takes in a prompt
as an input and returns the user's response.
prompt: This function is responsible for displaying the prompt
to the user. It takes in a prompt as an input and displays it
on the screen.
model: This function is responsible for processing the user's
response. It takes in a response as an input and returns a
processed version of the response.
parser: This function is responsible for parsing the user's
response into a format that can be used by the chatbot. It
takes in a response as an input and returns a parsed version
of the `response`.
The chain of functions is defined using the | operator, which
allows each function to receive the output of the previous
function as its input. In this case, the itemgetter function
retrieves the question from the input object, and then passes
it to the retriever function for processing. The prompt function
displays the prompt to the user, and the model function processes
the user's response before passing it to the parser function
for parsing.
I'm floored! This gave me an output better than I could have described it in the article. Believe me, I'm only getting started here.
There's a lot you can do with “AI as your pair programmer” so let me just go from explaining code, to jumping 10 steps ahead and creating full projects with AI. I went ahead and installed GitHub Copilot extensions in VSCode and pressed CMD_SHIFT_I to open the chat window. I also opened VSCode in a blank folder, and I set the mode to “Agent” and the model I'm using to GPT 4.1, as can be seen in Figure 4.
GitHub gives us little bit of Copilot for free, and VSCode and GitHub Copilot aren't the only shows in town. You should also check out Cursor AI and Windsurf.
Back to Copilot. I gave it the following prompt:
AI Query:
Create a nodejs application that serves REST APIs and creates a
route at /hello that returns a JSON greeting.
This set Copilot thinking. More than thinking, it's showing me exactly what it's doing and writing code for me. When it's ready for me to run an action, it prompts me to do so, as can be seen in Figure 5.
All I have to do now is hit Continue to run npm install. Copilot then instructs me to run npm start, which I do, and visit http://localhost:3000/hello, and my application is running. This can be seen in Figure 6.
I can now glance at the generated code and learn from it. In fact, I can ask the agent to make modifications to it. So, for instance, let's say:
AI Query:
Modify the "/hello" route to accept a user name as a querystring
`parameter`, and return "Hello <username>" as output.
Now when I visit, "http://localhost:3000/hello?name=sahil" the output is:
{"greeting":"Hello sahil"}
I encourage you to take this much further. Upload a screenshot of a ToDo app and ask it to generate the app for you. I assure you, you'll be amazed.
As amazed as you'll be, believe me, we're just getting started here.
Automated Code Review
Maintaining high code quality, consistency, and security is paramount for any software project. Manual code reviews are essential but can be time-consuming and prone to human oversight. AI-powered tools are transforming this by acting as vigilant guardians of your codebase.
Tools like Qodo (formerly CodiumAI), DeepCode AI (Snyk), and CodeRabbit analyze code for potential bugs, vulnerabilities, and style violations, and they even generate test cases.
There are many benefits of code reviews done by AI.
It'll help you detect bugs early. AI catches issues before they integrate deeper into the system, reducing costly fixes later. You'll improve code quality. AI ensures adherence to coding standards and identifies “code smells.” You gain faster feedback cycles. Imagine integrating AI in CircleCI. You'll get immediate insights, reducing the bottleneck often associated with manual reviews. And my favorite, AI offers enhanced security. You can proactively scan for common security vulnerabilities.
Teamwork is important, and I get requests to review pull requests all the time. I have my work to do, and yet here I am reviewing other's PRs. Ah well, this is how I stay on top of what others are doing. But there are days when I'm just too busy, or frankly I don't have the intelligence to review a PR. This is where AI can help me.
So for my next trick, I'm going to review a PR, just something real world that exists in the wild. I went ahead and cloned https://github.com/AzureAD/microsoft-authentication-library-for-python#, which is the repo for MSAL Python, and I see they have a moderately complex PR going on with some comments on it. Sounds like a perfect candidate to impress my friends with my (AI's) skills.
Just to show you that this is absolutely real world, this is the PR I'm reviewing. https://github.com/AzureAD/microsoft-authentication-library-for-python/pull/759. I had no coordination with the authors of that PR before this, and it's 3:30 AM on a Sunday night as I write this anyway.
My terminal chops aren't so great, and I keep forgetting how to check out a Git branch. No biggie. In the integrated VSCode terminal, I just press CMD_I, and describe in plain English what I wish to do, as can be seen in Figure 7.
This is so easy, it feels like cheating.
Reviewing the author's PR, I notice that the author has changed line 242 in application.py. Lazy person that I am, I select those lines, and ask Copilot to review it.
Copilot thinks for a while and immediately gives me two possible improvements. I'll show you the first comment for brevity, as can be seen in Figure 9.
I could just apply and change the code, but I want to appear smart to the author. So I'll take Copilot's suggestion and leave my review on the PR.
I left my comments on the PR, and my AI-based code review is done. All I need to do now is put eight hours on my timesheet and take a long lunch. This programming stuff is too hard.
Jokes aside, my boss will just give me more work. So let's stay focused.
Automated Documentation
Documentation is crucial for project maintainability and team collaboration, yet it's often the first thing to be deprioritized. Developers love to write code; developers hate writing documentation. AI tools are stepping in to automate this tedious but vital task. Tools like Mintlify can generate comprehensive documentation directly from your codebase, and general AI assistants like Notion AI can summarize meeting notes, create project briefs, or explain complex technical concepts.
This has many benefits.
You reduce manual effort. AI frees developers from the burden of writing and maintaining documentation.
You gain improved accuracy and currency. Documentation stays synchronized with code changes. Imagine a world where a merge of a pull request automatically generates documentation for you.
And you gain faster onboarding. New team members can quickly understand existing codebases. As a new team member myself, I find the ultimate source of truth to be code, not docs. But if I can paste a big function into an LLM and have it document it for me, I'm happy with those superpowers.
Back to the MSAL Python library, or feel free to pick any real codebase. I opened a particularly wiry part of the code, which is broker.py. I tried reading it, but it made my head hurt.
I asked GitHub Copilot with the following command in agentic mode.
AI Query:
Please document this code for me with diagrams in a file called broker.md.
In a matter of seconds, a new broker.md file is created for me. You just have to take my word for it; this is about the best documentation I've seen, complete with diagrams. I've pasted a partial screenshot of the documentation in Figure 10 for your review.
The diagrams you see are mermaid diagrams rendered visually inside VSCode using the mermaid extension.
Intelligent Troubleshooter
Debugging is an infamous time sink for developers. Debugging is twice as hard as writing code as some famous person once said. AI is increasingly capable of analyzing error messages, tracing code execution, and suggesting potential fixes or even refactoring solutions.
This is again yet another superpower you have with AI. AI will quickly point you to the likely source of errors. This is especially useful in large code bases that you may be unfamiliar with. AI can offer you intelligent suggestions. After it analyzes your code, it offers direct code fixes or alternative approaches. And finally, I don't know about you, but I really dislike taking an error message, searching Google for it, or spending time looking at complex stack traces. AI takes all that work away for me.
For my next code example, I used AI to conjure up an application that decodes a JWT token. And I deliberately introduced an error in it. The typical typo error. I won't tell you what the error is: Let it remain a mystery.
This is a React.JS app I had instructed AI to build for me, so I ran it using npm. When I run the application, I'm greeted with an ugly error, as follows.:
[plugin:vite:import-analysis]
Failed to resolve import "./Ap.jsx" from "src/main.jsx".
Does the file exist?
Now, those of you who spend your life in React immediately know what the issue is. But humor me here. There are React errors that can be quite cryptic. And, for a moment, let's assume I'm a developer who's not too familiar with React, I lied on my resume, got hired, and now I need to fix this bug so I can blame it on the last developer and get credit for fixing it.
So off I go to GitHub Copilot, and send the following prompt in agentic mode:
AI Query:
Please fix this bug "Failed to resolve import "./Ap.jsx" from
"src/main.jsx". Does the file exist?"
This sets Copilot thinking, and, in a matter of seconds, it fixes my error. The output of Copilot is shown in Figure 11.
And it even shows me in a diff format exactly the change it made in the file, as can be seen in Figure 12.
And I can verify that my application is back to working!
Using AI to Learn New Stuff
This tip is not specific to developers but is certainly applicable to developers. What's the number one thing developers have to do? Learn new stuff.
In today's fast-paced world, continuous learning isn't just an advantage, it's a necessity. Whether you're aiming to master a new programming language, delve into complex scientific theories, acquire a creative skill, or simply broaden your general knowledge, artificial intelligence has emerged as an incredibly powerful ally. Gone are the days of rote memorization or one-size-fits-all textbooks; AI offers personalized, interactive, and highly efficient ways to learn almost anything.
Because this tip isn't specific to developers, I'll intentionally pick a cumbersome topic. I do a lot of my learning from YouTube. There's a podcast I particularly like called Adam Taggart: Thoughtful Money. Let's pick one video as an example: https://www.youtube.com/watch?v=WSpR770JvXg
As much as I really like Adam's videos, who has one hour and forty-one minutes to listen to it without falling asleep? Look, this isn't my full-time job. Just give me the key points and skip the chit chat.
To save me time, I go to gemini.google.com and say Please summarize <video url>, and Gemini immediately gives me a nice little summary, with time stamps. This can be seen in Figure 13.
This is absolutely incredible. If there are points of interest, I can go to specific timestamps and listen to that part. This is a huge time saver. And I can take it a step further.
I went to notebooklm.google.com and created a new notebook and imported this video as source. Note that you can also import multiple sources. Now I can start interacting with that video by chatting with it. Let's give it a try.
I asked, “How did Vanguard's changing allocations in their target date funds affect the markets?” and notebooklm immediately gave me a very crisp to-the-point answer with references to sources as tool tips.
This video is full of knowledge and I wish to dive further. There's a lot you can do here: You can discover new sources, turn this into a briefing, or into a Q&A for a study guide. One of things I find really helpful is turning this knowledge into a mind map. I went ahead and generated a mind map for the content in this video.
In a matter of seconds, I'm given an interactive mind map that allows me to drill down to topics of interest, as can be seen in Figure 14.
I can keep expanding collapsing, learning things, and going to exact sources and quotes out of the video. In fact, I can combine many such sources together and create a unified knowledge base that I can learn from.
How would you, as a developer, benefit from this? First of all, if I need to learn something out of my area of expertise, like “How to maximize the growth of lettuce in my garden,” this is a huge time saver. But even in purely technical terms, if I want to learn about “Git,” I go ahead and ask the notebook to discover sources for “Git,” and ask it to generate a mind map.
The mind map for Git can be seen in Figure 15.
Or if I wanted to do something complicated with Git, I could simply ask a question in the interactive chat. For example, I asked:
AI Query:
"How do I rebase when I do a Git pull automatically?"
It gave me a proper answer with exact commands and explanations, along with sources and additional learning I should consider around this command. This can be seen in Figure 16.
Summary
The integration of AI tools into the developer workflow is no longer a futuristic concept but a present-day reality driving unprecedented levels of productivity. From intelligent code completion and automated testing to smarter documentation and even autonomous task execution, AI is transforming how software is built. By embracing these powerful tools judiciously, developers can amplify their capabilities, reduce tedious tasks, and dedicate more energy to the creative and strategic challenges that define truly impactful software engineering. The future of development is undeniably AI-augmented, and those who master its application will be at the forefront of innovation.
This article, although touching many aspects, barely scratches the surface of the possibilities. AI is incredibly powerful and it will supercharge your workflows. I highly encourage you to start integrating these into your daily coding habits.
Or, you know, just get left behind.
Until next time, happy coding!
