Semantic Search Beats Keywords for SaaS Help Centers, But Architecture Matters

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A software developer shares lessons learned building an ask-your-docs feature for a SaaS help center, recommending embeddings-based semantic search over keyword search for handling natural-language support queries. Semantic search converts both questions and document chunks into vectors, allowing conceptually related content to be matched even when exact words differ. The author warns that poor ingestion practices — such as splitting content by byte count or including repeated navigation elements — can cause AI models to return confident but irrelevant answers. A costly experiment that generated an unexpected $8,742 token bill underscored the importance of retrieving a small, clean set of source chunks before passing any content to a language model. The recommended architecture involves stripping templates, chunking clean content with metadata, storing vectors in a managed database, and maintaining traceable source references for every generated answer.

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