Ever tried to fit an elephant into a suitcase? That's what it felt like when we first attempted to create a serverless virus scanning solution for S3 uploads. Our journey took us through Lambda layers, container images, and some interesting discoveries about the limitations of serverless computing. Here's our story.
Imagine a world where your product documentation updates itself, always staying fresh and relevant. A world where customer support tickets automatically transform into valuable knowledge resources. Sound like science fiction? Not anymore.
In the fast-paced realm of customer support, keeping documentation up-to-date can feel like a never-ending battle. But what if we could harness the power of AI to turn this challenge into an opportunity?
A few weeks ago, my VP of Support and Customer Success approached me with an intriguing question: How can we utilize cutting-edge AI technology to create product documentation from the wealth of information hidden in our support tickets? Their quarterly project to update our knowledge base was looming, and they were looking for a way to automate the process, save time, and ensure our documentation always reflected the latest customer needs.
This sparked an exciting journey into the world of AI-powered documentation. In this article, I'll take you step-by-step through the process of building a system that uses Large Language Models (LLMs) to transform HubSpot support tickets into a dynamic, ever-evolving knowledge base.
Get ready to revolutionize your documentation process. Let's dive in!
In this post, we'll deploy Flagsmith on AWS using CloudFormation. Flagsmith is an open-source feature flag and remote configuration service. It allows you to manage feature flags and remote configuration for your applications.
At Autohost, we care about making data-driven decisions. I know many companies say this, but we mean it. We have a lot of data, and we want to use it to make better decisions. One of the issues is related to understanding the differences between signed contracts and billed invoices, and how to predict revenue. We can't tell if we are on track to hit our revenue goals, because we don't know how much revenue we will get from our existing customers as product usage changes.
About a year ago, one weekend, I decided to build an automatic note taker for my video calls. Having used OpenAI for several projects and recently enjoying a positive experience with Pilot AI, I set out to build a prototype.