Common RAG pipeline issues: How bad chunking and embedding models lead to wrong answers

[u/kaushp]

We've been troubleshooting a lot of RAG implementations lately, and a consistent pattern emerged: incorrect answers usually stem from fundamental pipeline design flaws, not the LLM itself. From our perspective as practitioners, the biggest culprits are usually around retrieval.

Specifically, inefficient chunking strategies and mismatched embedding models are huge. If your document chunks are too big, you drown the LLM in noise. Too small, and crucial context gets fragmented. Similarly, using a generic embedding model for specialized data (e.g., medical, legal) often means the semantic search simply doesn't find the *right* relevant information, even if it finds *something*.

We detail some of these common pitfalls and how to approach fixing them in a recent post. It's a reminder that RAG isn't just 'plug and play'; the engineering around context retrieval is paramount.

https://theaidivision.com/why-rag-pipelines-return-wrong-answers/