AWS developer Banjo Obayomi harnesses Amazon’s generative AI tools to get answers and solve problems, while helping other engineers learn and build on Amazon Web Services.

I’ve been interested in software engineering ever since I got hooked on video games as a kid. I studied computer science in college at the University of Maryland, which was hard work. I didn’t really get it at first—until I built first my first app and saw how much creativity was involved in the process.

I went on to work for more than 10 years as a software engineer, initially coding for government contractors and later running cloud infrastructure and data analytics platforms for technology companies.

A photo of Banjo Obayomi, senior developer advocate, AWS

These days, I work at Amazon Web Services (AWS) as what’s known as a developer advocate—it’s a role that allows me to connect with other engineers help them learn about AWS tools. I spend much of my time sharing my knowledge at workshops, meetups, and AWS Community Day events.

Additionally, I do a lot of my own experimenting, building with AWS’s latest tools and creating real-world applications for the newest generative AI tools that we are building on AWS.

One tool I’ve been using recently is Amazon Q, which AWS just announced is now generally available. It’s a generative artificial intelligence-powered assistant that’s designed to revolutionize the future of work for employees across every organization and department.

When Amazon Q was first previewed at re:Invent last year, I knew I needed to give it a try. Using generative AI, it can give you fast answers to questions, solve problems, and generate content.

It’s useful for more than just developers like me, of course. IT professionals and business users of all kinds use Amazon Q to extract relevant information to streamline tasks and speed up decision-making.

I think this is one of the best generative AI tools that can help to spark creativity and innovation at work.

Here are three ways I’ve been using Amazon Q to do just that:

Page overview

1. Amazon Q helps me understand a new (or old) source code quickly

2. I can debug code and fix errors with Amazon Q—right in the AWS console

3. I run queries with Amazon Q, without using technical language

1. Amazon Q helps me understand a new (or old) source code quickly

I’m a big fan of the Slay the Spire video game, where the goal is to ascend a spire of multiple floors while battling enemies. Recently, I decided I wanted to build a large language model (LLM) bot that would be able to play the game by itself.

A screenshot of Amazon Q Developer in preview, assisting with coding of a video game.

Amazon Q Developer chatting about the functionality of code within Slay the Spire.

I was able to find a five-year-old codebase (the body of source code for a given game or application) that could help me do this, but it was lacking documentation and it had bugs.

In just three hours I was able to use Amazon Q to help me understand code, allowing me to generate my own code and debug issues as they arose to build my bot. Without the help of this assistant, it would have taken me a day or two to do this work, not just a few hours. Here's the play-by-play of how I did that.

2. I can debug code and fix errors with Amazon Q—right in the AWS console

If you code as often as I do, it’s inevitable that you’ll make a mistake or encounter an error as you build.

When you’re building applications, you may not know how to use all the capabilities that are available to you. If I have a question on how to do something, I use Amazon Q to guide me on how to do a specific task.

Sometimes I talk to engineers who don’t know where to begin, or are afraid to touch things because there are so many possibilities. Amazon Q can provide assistance and guide you on how to take advantage of all the capabilities of the console, using something as simple as a natural language prompt.

3. I run queries with Amazon Q, without using technical language

When building workshops, it's common to include exercises that involve querying data. SQL (a language for database creation and calculations) is often used for this purpose. While SQL is powerful, it can be challenging for developers to remember the correct syntax, keywords, and database names.

A photo of Banjo Obayomi, senior developer advocate, AWS, presenting at a workshop. There are several participants sitting around a conference room with their laptops on the table.

When I was building a workshop recently, one of the modules focused on writing SQL queries to analyze user engagement with different products. I wanted to show developers how much easier it is to use Amazon Q for this task instead of writing the queries from scratch.

If you're a seasoned SQL developer, you could write that query easily. But with Q, developers could ask that question in simple, natural language, and see it return the answer they needed.

I’m excited to see how Amazon Q will continue to evolve. I know it has many capabilities that can be used in helping build solutions, and I’m looking forward to using it in more parts of the software development life cycle—from feature development to code transformation.