𝗬𝗼𝘂𝗿 𝗥𝗔𝗚 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲𝗱 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀 𝗯𝘂𝘁 𝗦𝘁𝗶𝗹𝗹 𝗚𝗮𝘃𝗲 𝘁𝗵𝗲 𝗪𝗿𝗼𝗻𝗴 𝗔𝗻𝘀𝘄𝗲𝗿

Your retriever returned the right documents. Your similarity scores look good. Yet, the answer remains wrong.

If you build RAG systems, you know this problem. It survives every retrieval upgrade.

You might try these fixes:

These methods aim to find documents more similar to your query. They help if the retriever misses the right document. They fail when the retriever finds the right document but the answer stays wrong.

Similarity only asks one question: Is this chunk about the same topic?

It does not ask: Does this chunk contain the facts needed to answer the question?

A chunk can have the same vocabulary and subject but lack the necessary facts. If you give a model a pile of on-topic text, it will write a fluent answer. It might even include citations. This is cosmetic grounding. The text is nearby, but it does not support the answer.

High similarity does not guarantee a correct answer. You asked for related text. The system found it. You did not ask if the text was sufficient.

Stop treating retrieval output as evidence. Treat it as candidate material. This material must pass an explicit evidence check before it supports an answer.

Add a step between retrieval and generation. Ask: Does this set contain the facts required for this answer? If no, the system must abstain. It should return nothing instead of a confident guess.

Only allow sufficient evidence through. This separates a RAG demo from a RAG system you trust in production.

I write about why production RAG fails. I focus on query, evidence, and output boundaries under security and model constraints.

Source: https://dev.to/mofuteq/your-rag-retrieved-the-right-documents-but-still-gave-the-wrong-answer-2pg5

Optional learning community: https://t.me/GyaanSetuAi