Machine Learning

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. ...

Introduction Modern search platforms rarely rely on a single retrieval strategy. In practice, a production system often supports multiple search modes, such as: Keyword search for exact identifiers and error codes Semantic search for conceptual or explanatory queries Structured search where natural language is translated into a query DSL (e.g., Lucene) The challenge is not implementing these search engines individually — it is deciding, at query time, which engine to route a user query to. ...