My current active project with generative AI at my workplace is looking to incorporate our API documentation into our search bot, Penny. There were a few tweaks I had to make compared to our first user documentation search project that I thought were interesting enough to share.

So you’ve convinced your employer to allow you to play around with generative AI, and you’ve managed to put something neat together. What’s the next steps? The gap from “neat experiment on my laptop” to “production-ready application” is often wide, and with the new landscape of generative AI models it can be even more daunting than before. Here’s a few tips to keep in mind.

During a recent code review with a co-worker, we were discussing ways of making some code more testable, and stumbled across the concept of “orchestrating” code and “domain” code. I thought it was one of the more clever concepts we had talked about recently, so figured it’d be a good subject for a blog post!

After going over the benefits and differences of vector search in my previous post, I’d like to go over a real world example - embedding documentation, putting it into a vector database, performing searches against the database, and responding to a question using vector search, retrieval-augmented generation, and language models.