OmbuLabs Blog

A few months ago I attended NERCOMP for the first time and I got to connect with IT professionals in the higher ed space. This is the first article in a series collecting my takeaways from some of the most interesting AI-related sessions at the conference.

I tried to focus on real-world examples of how higher ed professionals are thinking through and applying AI and ML, with all their limitations, in day-to-day academic life.

Keep Your Data Close and Your AI Closer: Local AI in the Academy

In their presentation, Gerol Petruzella & Trevor Murphy (Williams College) presented 3 case studies that leveraged AI to solve real scenarios at their institution.

These were 3 privacy-first, cloud-free applications of local AI to support pedagogy, accessibility, and student experimentation.

In my previous post, An LLM-based AI Assistant for the FastRuby.io Newsletter, I introduced an AI-powered assistant we built with Sinatra to help our marketing team write summaries of blog posts for our newsletter.

In this post, I’ll go over how we implemented semantic search using pgvector and Sequel to fetch examples of previous summaries based on article content.

Semantic search allows our AI assistant to find the most relevant past examples, given meaning and context, when generating new summaries. This helps ensure consistency in tone and style while providing context-aware results that will serve as better examples for the large language modal (LLM) to generate new summaries, improving the quality of the generated output.