I Rebuilt My RAG Pipeline Without LangChain — What Got Better and What Got Worse

A developer shares their experience transitioning from using LangChain to a custom-built RAG (Retrieval-Augmented Generation) pipeline. While LangChain is useful for rapid prototyping, the author argues that production-grade RAG systems require more granular control over retrieval, chunking, and observability. By moving away from the abstraction layer, the author gained significant improvements in debugging, retrieval accuracy, and cost management. Key strategies included implementing a query planner, adopting hybrid search for exact identifiers, and using reranking as a quality gate. Although the transition required writing more 'glue code' and handling complex data engineering tasks, the author concludes that owning the retrieval core is essential when product reliability depends on answer quality. The article serves as a practical guide for developers deciding whether to keep or remove high-level frameworks from their production AI applications.