--- title: Your RAG Isn’t Hallucinating. Retrieval Handed It the... description: Most RAG hallucination complaints are actually retrieval failures — the model answered faithfully from the wrong chunk. Standard single-vector dense search... canonical: https://daily.dev/posts/your-rag-isn-t-hallucinating-retrieval-handed-it-the-wrong-chunk--tw9lco7qx twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Your RAG Isn’t Hallucinating. Retrieval Handed It the Wrong Chunk. | daily.dev og:description: Most RAG hallucination complaints are actually retrieval failures — the model answered faithfully from the wrong chunk. Standard single-vector dense search... og:url: https://daily.dev/posts/your-rag-isn-t-hallucinating-retrieval-handed-it-the-wrong-chunk--tw9lco7qx og:image: https://api.daily.dev/og/posts/TW9lco7Qx.png og:image:alt: Your RAG Isn’t Hallucinating. Retrieval Handed It the Wrong Chunk. og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Your RAG Isn’t Hallucinating. Retrieval Handed It the Wrong Chunk. **[Medium](https://daily.dev/sources/medium_js)** · 3 min read · 1 upvotes · 0 comments ## Summary Most RAG hallucination complaints are actually retrieval failures — the model answered faithfully from the wrong chunk. Standard single-vector dense search averages token information away, causing it to miss clause-level or value-level queries. Two techniques address this: ColBERT's late interaction keeps per-token vectors and scores via MaxSim for precise token-level matching, while ColPali skips PDF parsing entirely by embedding page images as patches for visual retrieval of tables and charts. The recommended approach is to start with dense+BM25 hybrid and a reranker, then escalate to late interaction or visual retrieval only when evaluation proves the specific failure mode exists in your corpus. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why does my RAG system give wrong answers even though the LLM seems to be hallucinating? The failure is usually retrieval, not the model: single-vector dense embeddings average an entire chunk into one vector, losing the specific tokens an answer depends on, so a worse chunk outranks the correct one and the model faithfully answers from the wrong context. This shows up on queries like which clause covers a specific case or what value is in a specific column. _Teams debugging RAG accuracy issues often compare retrieval fixes like this on daily.dev._ ### What is ColBERT late interaction and when should I use it instead of single-vector embeddings for RAG? ColBERT keeps a vector per token instead of one vector per chunk, scoring relevance by matching each query token against its best-matching document token and summing the scores (MaxSim). This recovers clause-level and value-level answers that single-vector search misses, at the cost of turning a 300-token chunk into 300 vectors, requiring multi-vector indexing and more storage. It should be added only after evaluation shows specific-token queries failing on single-vector search. _Evaluating retrieval architectures like ColBERT before committing storage budget is easier with daily.dev's technical coverage._ ### How does ColPali handle tables and charts that PDF parsers destroy before RAG retrieval sees them? ColPali skips text extraction entirely by feeding the page image directly to a vision-language model, producing per-patch embeddings, then applying the same late-interaction matching used by ColBERT to image patches instead of text tokens. A query like what was Q3 revenue in the segment table matches the visual region of the table directly, avoiding the column merging and caption loss that traditional text extractors cause on financial reports, scientific papers, invoices, and slide decks. _daily.dev helps engineers tracking emerging retrieval techniques like visual RAG stay ahead of parsing pitfalls._ ## Similar posts on daily.dev - [Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent](https://daily.dev/posts/most-rag-hallucinations-are-retrieval-failures-how-the-retrieval-brick-decides-what-the-model-can-i-8ajbd1sn7) · Towards Data Science · 2 upvotes · 0 comments - [The AI Series: The Hardest Part of RAG, Part 2 — Why Perfect Chunks Still Give You Bad Retrieval](https://daily.dev/posts/the-ai-series-the-hardest-part-of-rag-part-2-why-perfect-chunks-still-give-you-bad-retrieval-4qoyry9kl) · Medium · 1 upvotes · 0 comments - [RAG Explained Simply with a Real Project](https://daily.dev/posts/rag-explained-simply-with-a-real-project-pgy15jzg7) · freeCodeCamp · 23 upvotes · 1 comments - [Your LLM Is Only as Good as What It Retrieves](https://daily.dev/posts/your-llm-is-only-as-good-as-what-it-retrieves-q3fmcuq2k) · Weaviate · 0 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#rag](https://daily.dev/tags/rag), [#vector-search](https://daily.dev/tags/vector-search) [View this post on daily.dev](https://daily.dev/posts/your-rag-isn-t-hallucinating-retrieval-handed-it-the-wrong-chunk--tw9lco7qx) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Your RAG Isn’t Hallucinating. 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