Introduction
As a developer in a modern organization, I’ve discovered that Generative AI (Gen AI) tools can massively streamline my workflow and spark new ideas. Whether I’m generating code snippets, designing an architecture, or making sense of cryptic logs, these tools have become my go-to assistants. In this blog post, I’ll walk you through how I use Gen AI—tools like GitHub Copilot, ChatGPT, Claude, and DeepSeek—throughout my daily tasks.
💻 Code Generation
</> Inline Code Suggestions with GitHub Copilot
My first line of assistance often comes from GitHub Copilot. It’s fantastic for inline code generation and small snippets, especially when I’m quickly prototyping or writing test cases for newly created functions. Copilot’s ability to anticipate what I might write next saves time and helps me maintain my coding flow.
😵💫 Complex Code with ChatGPT, Claude, and DeepSeek
For more complex problems—anything beyond 15 lines of code with multiple loops, arrays, and hash maps—I rely on tools like ChatGPT, Claude, or DeepSeek. These larger blocks of code usually require an iterative approach:
- Initial Prompt: I describe the logic, data structures, and desired outcome in detail.
- Test and Tweak: I run the generated code through multiple scenarios. If it fails (and it often does the first time), I provide the error details back to the AI.
- Refine: The AI adjusts the code based on my feedback. This iterative loop continues until I have working code.
Occasionally, for very specific or edge-case scenarios—like transferring a file using SFTP with both password and PEM files. These tools might not have the perfect solution. In those instances, I do old-fashioned research, dive into documentation, or ask peers for assistance.
☁️ AWS Stack Creation
🖨️ Generating SAM Templates and CDK Scripts
Setting up AWS infrastructure can be a tedious process. Here, GitHub Copilot, ChatGPT, Claude, and DeepSeek shine by quickly generating skeleton AWS SAM templates or AWS CDK scripts. However, while these tools give a great starting point, the journey doesn’t end there.
🔄 Iterative Deployment and Troubleshooting
Deploying to AWS can throw error messages that require you to tweak your configurations (sometimes repeatedly). At this point, Gen AI can be a helper, but not a silver bullet. When it fails, I revert to manual strategies such as Googling or reading AWS docs. For example, I once tried to create a stack that would pass an API Gateway POST request directly to SQS, and the Gen AI suggestions needed heavy manual adjustments to make it work seamlessly.
🏗️ Architecture
💡 Brainstorming and Prototyping
Coming up with a solid architecture for a new feature can be daunting. Using ChatGPT, Claude, and DeepSeek, I start by describing all the systems involved (for example, UI, Auth, Commerce APIs) and the overall feature requirements. Then I specify the constraints and must-haves—like using DynamoDB, Serverless AWS components, or particular libraries. This is an iterative process and requires a lot of refinement.
🔄 Iterative Refinement
- Initial Sketch: The AI proposes a high-level architecture.
- Feedback Loop: I refine it, asking the AI to focus on certain aspects or discard ideas that aren’t relevant.
- Final Direction: Even if I don’t follow the AI’s suggestions to the letter, they often spark ideas or highlight considerations I hadn’t thought of.
Sometimes, simply listing out the systems and describing the feature leads to insights about possible directions or solutions. The AI might suggest a route that’s not exactly what I want, but it often kickstarts my thinking process.
📝 Log Parsing
🔎 Making Sense of Cryptic Logs
When I run into cryptic logs—especially from applications with complicated processes Gen AI tools can help decode them.
In one case, I was handling multiple authentication mechanisms with SFTP, I did not have much experience in that domain and I didn't spend enough time learning about it too. Online examples weren’t much help. I could use tools like FileZilla to perform the same operations but I was not able to handle it via code as my approach was wrong. Then is used the debug option on FileZilla and examined the logs using ChatGPT. The explanation of logs gave me crucial insights into how those multiple auth modes worked together.
🌐 Other Use Cases
- Responding to Emails: Sometimes you don’t know how to phrase a response. A quick Gen AI prompt can suggest a professional email template.
- Formatting Documentation: Need a polished summary or a visually appealing layout? Gen AI can reformat or enhance your text in seconds.
- Suggesting Emojis or Headings: A little flair in documentation or internal wikis goes a long way.
- Creating Imagery for Documentation: Tools that generate AI-based images can add an extra layer of drama or clarity to your docs.
- Creating Avatars: I usually use image generation tools like Stable Diffusion to give myself new looks, just to spice up my display image on collaboration tools like Slack, Zoom etc. I do the same to my colleagues too, its gets a good laugh, lightens the mood and creates a healthy work environment.
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🎉 Conclusion
Generative AI has become a trusted companion in my daily development workflow. From quickly generating code and AWS scripts to parsing logs and even writing email responses, it helps me work faster and more creatively. Of course, it’s not a one-size-fits-all solution. Complex edge cases and highly specialized scenarios still require manual research, experimentation, and sometimes plain old trial and error.
But overall, incorporating Gen AI into my day-to-day routine has dramatically streamlined everything from small tasks like formatting documentation to big undertakings like designing new features or setting up AWS infrastructure. It’s exciting to see where these tools will go next—and how they’ll continue to shape the future of software development.
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