We always knew that AI could be a big deal for our product, but we weren't sure where to start. We had a lot of ideas, but we wanted to make sure our first AI feature was something that would actually help our users.
After a lot of brainstorming, we decided to build a tool that could automatically grade assessments. It was a big project, but it had the potential to save our users a ton of time. Here's a quick look at how we did it.
Finding the Right Model
The first step was to find the right machine learning model for the job. We experimented with a few different approaches, but we ended up fine-tuning a large language model (LLM) on our own data. This gave us the best results and allowed us to tailor the model to our specific needs.
Setting Up the Infrastructure
Once we had a model we were happy with, we had to figure out how to get it into production. We built a scalable API to serve the model, and we set up a CI/CD pipeline to make it easy to update the model as we got more data. We also put a lot of work into monitoring the model's performance to make sure it was always giving accurate results.
What We Learned
Building our first AI feature was a huge learning experience. We learned a lot about what it takes to build and deploy a machine learning model in a real-world application. It wasn't easy, but it was worth it. We've already gotten a lot of great feedback from our users, and we're excited to see what else we can do with AI in the future.