Yule LOGIC – 12 Days of Fun LLM-Related Posts by LOGIC, Inc.
It’s the holiday season, and you’re on the hunt for the perfect peppermint-infused sugar cookie recipe to wow your guests. But sifting through thousands of online recipe sites can be tedious. What if you could simply type your wish into a search bar and instantly see the best holiday cookie recipes? Even better – ranked and filtered precisely by your preferences and the ingredients you already have.
We’re going to walk through how to do this using modern semantic retrieval techniques. If we were using traditional information retrieval, we would create simple inverted indexes that map words to recipes and then find recipes based on keyword matching – but we live in the Renaissance Age of natural language processing and no longer need to rely exclusively on discrete string matching.
We can now retrieve results based on their abstract meaning and semantics, and even filter based on fuzzy criteria such as “I don’t want it to take too long to bake”. This kind of semantic retrieval is used in many LLM systems these days, but embeddings are still a lesser known concept to many LLM practitioners, and rerankers even less so.
