Developer Rebuilds RAG Pipeline Without LangChain, Finds Tradeoffs in Control vs Convenience

A software developer rebuilt a production-grade Retrieval-Augmented Generation (RAG) pipeline from scratch after finding that LangChain's abstractions made it difficult to diagnose incorrect but plausible-sounding answers. The core issue was that hidden pipeline layers obscured why retrieval favored wrong or outdated documents, making debugging slow and indirect. Removing LangChain improved retrieval control, observability, evaluation, and cost discipline by making each pipeline stage explicit and inspectable. However, the shift also introduced more manual work, including document loading, integration maintenance, and writing glue code that frameworks typically handle automatically. The developer's current approach recommends using high-level tools for prototyping but taking direct ownership of the retrieval core when answer quality is critical to the product.