---
title: "Discussion on \"Where RAG Fails: Understanding the Limitations\" | Hashnode"
description: "Discussion on \"Where RAG Fails: Understanding the Limitations\". RAG helps LLMs answer questions using external knowledge, but it does not make them always correct."
url: "https://hashnode.com/posts/where-rag-fails-understanding-the-limitations/6a567fa974d097f15b3ec8d8"
---

[Feed](https://hashnode.com/feed) /

Discussion

# [Where RAG Fails: Understanding the Limitations](https://princekumar-engineer.hashnode.dev/where-rag-fails-understanding-the-limitations?utm_source=hashnode&utm_medium=feed)

RAG helps LLMs answer questions using external knowledge, but it does not make them always correct.

Jul 14 · 8 min read · princekumar-engineer.hashnode.dev  
Updated Jul 16  
Read the article

-   [#rag](https://hashnode.com/tag/rag)
-   [#ai](https://hashnode.com/tag/ai)
-   [#chaicode](https://hashnode.com/tag/chaicode)
-   [#cohort](https://hashnode.com/tag/cohort)
-   [#chatgpt](https://hashnode.com/tag/chatgpt)
-   [#llm](https://hashnode.com/tag/llm)

## Written by

[PRINCE KUMAR](https://hashnode.com/@farshoreprince)

@farshoreprince

  
[View profile](https://hashnode.com/@farshoreprince)

## Responses 1

Markdown supported · ⌘↵ to post

[Mateo Ruiz](https://hashnode.com/@mateoruiz)

[Jul 15](https://hashnode.com/posts/where-rag-fails-understanding-the-limitations/6a567fa974d097f15b3ec8d8/comment/6a5768e4d9dbf0f8aea11410)

This is a solid overview, especially the point that RAG reduces hallucinations but doesn't eliminate them. One thing I'd add is that most production issues aren't caused by the LLM they're caused by retrieval itself. Poor chunking, weak metadata, missing hybrid search, stale embeddings, or lack of reranking can all make the model look "wrong" even when it's behaving exactly as expected.

We've found that treating retrieval as an evolving system with retrieval evaluation, feedback loops, and continuous re-indexing usually delivers bigger gains than swapping to a larger model. Production RAG is much more about improving retrieval quality than chasing benchmark scores.