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RAG systems fail before the model: developers urged to fix pipeline

By PulseAugur Editorial · [3 sources] ·

Retrieval-augmented generation (RAG) systems often fail due to issues earlier in the pipeline, rather than the language model itself. Common problems include poorly parsed documents, incorrect chunking of information, and failure to retrieve relevant passages. To address these failures, developers are advised to implement an evaluation loop that involves creating a set of golden questions, modifying one stage of the RAG process at a time, and scoring retrieval and answers separately before releasing any changes. AI

IMPACT Developers can improve RAG system accuracy by focusing on data preparation and retrieval rather than solely on the LLM.

RANK_REASON The cluster discusses research findings and practical guides on improving RAG systems, focusing on technical aspects and evaluation loops.

Read on Towards AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

RAG systems fail before the model: developers urged to fix pipeline

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The cluster discusses research findings and practical guides on improving RAG systems, focusing on technical aspects and evaluation loops.
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COVERAGE [3]

  1. Towards AI TIER_1 English(EN) · Sumit Vedpathak ·

    Part 1 — Candidates: Why Your RAG Returns the Wrong Chunks (and How to Get the Right Ones)

    <div class="medium-feed-item"><p class="medium-feed-image"><a href="https://pub.towardsai.net/part-1-candidates-why-your-rag-returns-the-wrong-chunks-and-how-to-get-the-right-ones-9d4e6ec194e0?source=rss----98111c9905da---4"><img src="https://cdn-images-1.medium.com/max/1536/1*hL…

  2. dev.to — LLM tag TIER_1 English(EN) · aiunplugged ·

    RAG chunking — 5 strategies with runnable Python

    <p>Originally published on <a href="https://aiunplugged.in/blog/rag-chunking-strategies/" rel="noopener noreferrer">aiunplugged.in</a> — cross-posting for the Dev.to community.</p> <p>Chunking is the step that most RAG tutorials gloss over — "just split your documents into 500-to…

  3. dev.to — LLM tag TIER_1 English(EN) · Jessica Williams ·

    Your RAG App Isn't Failing at the Model: A Developer's Guide to Chunking, Retrieval and the Evaluation Loop That Fixes It

    <h2> Quick Answer </h2> <p>When a RAG app gives a wrong answer, the model is often the last place to look. The usual causes sit earlier in the pipeline: documents parsed badly, chunks cut in the wrong places, the right passage never retrieved, or a user shown text they should not…

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