Why Most RAG Pipelines Fail (And How to Fix Them)

Author(s): Sai Bhargav Rallapalli

Originally published on Towards AI.

From chunking to retrieval to evaluation, here’s how to turn RAG from demo to production-ready.

Ever built a Retrieval Augmented Generation (RAG) pipeline that shined in demo but crumbled in production? You’re not alone. Here’s a story I’ve experienced and witnessed many times:

The article discusses the common failures of Retrieval Augmented Generation (RAG) pipelines that initially perform well in demos but struggle in production environments. It highlights issues such as improper chunking of documents, insufficiently tuned embeddings, and ineffective retrieval methods. Solutions include using semantic chunking for context preservation, employing domain-specific embeddings, and enhancing retrieval strategies through hybrid methods and reranking. Ultimately, the article emphasizes the necessity of thorough evaluation and attention to cost and compliance to transition from a basic prototype to a fully operational system.

Read the full blog for free on Medium.

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Published via Towards AI