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Can GPT fix the Cloud Deployment Debugging Loop?

After getting over our initial skepticism, here's why we turned to GPT
September 13, 2023

Let's be real: How many developers do you know who relish the thought of trawling through countless AWS screens for error logs? The general consensus from our user conversations suggests that the answer is, well, not many. In a bid to make developers' lives a tad bit easier, we made a strategic decision. Why not streamline the AWS experience to a point where you barely have to visit it?

Our recent drive towards observability is a big step toward that vision. Our build pipelines now have discrete build/test/deploy steps, providing a clearer, more intuitive user experience. Websockets provide faster feedback loops and better data synchronization. Failed pipeline stages now have seamlessly integrated AWS/GCP logs in the UI.

During a sneak peek of the new pipeline UI with a favorite customer, the feedback was clear: "Awesome, but when things hit a snag, guide me. What's the next step?" It's a challenge, no doubt, but one we're ready for. When things go wrong, that's when a product’s mettle is truly tested. Observability is just the first step of making the cloud debugging journey frictionless.

  • Locate the source of the failure (Solved ☑️)
  • Determine the root cause 
  • Formulate a solution
  • Redeploy (Solved ☑️)

We got excited about a seamlessly integrated agent that could decipher logs, identify the point of failure, and propose a solution. After getting over our initial skepticism, here's why we turned to GPT:

Impact vs. Effort – With our compact team size and the intricate nature of the problem, GPT's cost-effective integration was a no-brainer.

Quality Inputs – We aren't starting from scratch. Our arsenal included logs from CodeBuild, ECS tasks and events, and the code differential between the build and the live deployment.

Upside Potential – Even a slight enhancement in user experience can make all the difference.

The troubleshoot agent adeptly pointing out the root cause

Crafting and iterating on the prompt was a fun bit of mad science. The early stages showed promise but required refinement. We grappled with issues of repetitiveness, overlooked details, vague instructions, and inconsistent feedback length. The challenge wasn’t just about sound debugging; it was about molding GPT's outputs to align with our desired style, syntax, and format. Anyone who has used chatGPT knows that wonkiness is inevitable, so we decided to use a design language to signal the AI integration. A special icon, text color, and typing animation serve as both a brand signature and a reminder to approach the tool’s insights judiciously.

While we are still nurturing this feature, our early experiences have stoked our enthusiasm. The integration of GPT for deciphering cloud logs is not just about simplifying a task; it's about redefining the debugging experience. As we continue to fine-tune and evolve, we're driven by one goal: making the developer's journey smoother, smarter, and more intuitive.