Beyond Generic RAG: Teaching Your Search Engine to Speak Your Domain’s Language
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. ...