Information Retrieval

Why I Built This: When “Good Search” Still Feels Wrong Most enterprise search stacks do a decent job at matching words. OpenSearch (and similar engines) commonly rely on lexical ranking families like TF‑IDF/BM25. These are strong baselines, but they tend to miss something important: user-specific intent. Three failure modes show up repeatedly in real systems: Ambiguity: the same term can mean different things to different users (or even the same user on different days). Intent drift: users’ interests change over time; yesterday’s context matters. One-size-fits-all ranking: a uniform ranking is applied across all users even though patterns differ by role, team, or experience level. A typical reaction is: “Let’s use embeddings” (dense retrieval, sentence transformers, vector databases, LTR models). That can work—but it also brings cost, latency, new infra, and sometimes reduced explainability. ...

Modern search systems increasingly rely on vector embeddings to find “similar” documents. This works remarkably well—until it doesn’t. If you have ever tried to apply off-the-shelf embeddings to a highly domain-specific corpus, you may have noticed a recurring problem: the results are linguistically reasonable but semantically wrong for your domain. This post explores why off-the-shelf embeddings often fail in the “real world” and how to build domain-adaptive embeddings that actually understand your business logic. ...