--- title: Why AI Gets Facts Right, Answers Wrong: GraphRAG Fixes It description: AI agents built on retrieval-augmented generation can cite technically accurate facts while still producing the wrong answer, because the necessary connecting... canonical: https://daily.dev/posts/why-ai-gets-facts-right-answers-wrong-graphrag-fixes-it-enq6jmdac twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Why AI Gets Facts Right, Answers Wrong: GraphRAG Fixes It | daily.dev og:description: AI agents built on retrieval-augmented generation can cite technically accurate facts while still producing the wrong answer, because the necessary connecting... og:url: https://daily.dev/posts/why-ai-gets-facts-right-answers-wrong-graphrag-fixes-it-enq6jmdac og:image: https://api.daily.dev/og/posts/ENq6JMdAc.png og:image:alt: Why AI Gets Facts Right, Answers Wrong: GraphRAG Fixes It 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. # Why AI Gets Facts Right, Answers Wrong: GraphRAG Fixes It **[Salesforce Engineering](https://daily.dev/sources/salesforceeng)** · 7 min read · 3 upvotes · 0 comments ## Summary AI agents built on retrieval-augmented generation can cite technically accurate facts while still producing the wrong answer, because the necessary connecting facts live across separate systems (a catalog, a wiki, a CRM) and never get assembled during retrieval. Salesforce engineers describe this as the 'flat bag of chunks' problem and explain how GraphRAG addresses it by extracting entities and relationships into a knowledge graph, enabling multi-hop retrieval that follows connections (e.g., product to classification to policy to eligibility conditions) that vector or keyword search alone would miss. The piece walks through diagnosing three distinct failure points: missing context in retrieval, an incomplete graph blueprint (TBox/ABox), and missing pointers between graph entities and structured records, using a washing-machine return exception as a running example from the Agentforce implementation. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why does a RAG-based AI agent give a wrong answer even when the facts it cites are accurate? The agent retrieves individually correct facts but fails to assemble the connecting facts that determine which policy or rule actually applies, a failure engineers call the 'flat bag of chunks' problem. For example, a return policy and purchase date can both be accurate while missing an eligibility exception tied to product classification and membership tier stored in separate systems. _Developers debugging RAG pipeline gaps discuss retrieval architecture patterns like this on daily.dev._ ### What is multi-hop retrieval in GraphRAG and why does it matter for AI agents? Multi-hop retrieval means an agent follows successive relationships in a knowledge graph to reach necessary information beyond initial similarity matches, rather than relying only on vector or keyword search. For instance, a product ID connects to a classification, which connects to a policy, which connects to further eligibility checks like membership tier and purchase amount, revealing facts the original question never mentioned. _Teams designing knowledge graphs for AI agents track these retrieval patterns on daily.dev._ ### What is the difference between the TBox and ABox in a knowledge graph used for GraphRAG? The TBox defines entity types and relationships, the structural blueprint of how concepts like membership tier and purchase amount relate to a policy, while the ABox contains the actual instance data, such as a specific customer's Gold membership and a particular purchase amount. A graph missing a rule in its TBox cannot be fixed by adding more ABox records, since the omission is structural, not data-related. _Engineers structuring knowledge graphs for retrieval-augmented systems follow this kind of design guidance on daily.dev._ ## Similar posts on daily.dev - [Knowledge graph RAG: structured retrieval for AI agents](https://daily.dev/posts/knowledge-graph-rag-structured-retrieval-for-ai-agents-k6cymxiik) · Redis · 1 upvotes · 0 comments - [What is GraphRAG: Complete guide \[2025\]](https://daily.dev/posts/what-is-graphrag-complete-guide-2025--3mdxbesnn) · Meilisearch · 5 upvotes · 0 comments - [GraphRAG: How AI Answers Questions Hidden Across Many Documents](https://daily.dev/posts/graphrag-how-ai-answers-questions-hidden-across-many-documents-y6prjknjz) · ByteByteGo · 7 upvotes · 1 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#rag](https://daily.dev/tags/rag), [#salesforce](https://daily.dev/tags/salesforce) [View this post on daily.dev](https://daily.dev/posts/why-ai-gets-facts-right-answers-wrong-graphrag-fixes-it-enq6jmdac) ```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":"Why AI Gets Facts Right, Answers Wrong: GraphRAG Fixes It","url":"https://daily.dev/posts/why-ai-gets-facts-right-answers-wrong-graphrag-fixes-it-enq6jmdac","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/why-ai-gets-facts-right-answers-wrong-graphrag-fixes-it-enq6jmdac"},"datePublished":"2026-08-31T17:41:36.289Z","dateModified":"2026-08-31T18:37:00.708Z","description":"AI agents built on retrieval-augmented generation can cite technically accurate facts while still producing the wrong answer, because the necessary connecting...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/aa5046d5f3c558da918e0632158b9c52?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/aa5046d5f3c558da918e0632158b9c52?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Salesforce Engineering","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Salesforce Engineering","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/513375817ec24d97b9cdb5efd16f87f2","url":"https://daily.dev/sources/salesforceeng"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/why-ai-gets-facts-right-answers-wrong-graphrag-fixes-it-enq6jmdac","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":3},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-agents,rag,salesforce","timeRequired":"PT7M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Salesforce Engineering","item":"https://daily.dev/sources/salesforceeng"},{"@type":"ListItem","position":3,"name":"Why AI Gets Facts Right, Answers Wrong: GraphRAG Fixes It"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/why-ai-gets-facts-right-answers-wrong-graphrag-fixes-it-enq6jmdac#faq","mainEntity":[{"@type":"Question","name":"Why does a RAG-based AI agent give a wrong answer even when the facts it cites are accurate?","acceptedAnswer":{"@type":"Answer","text":"The agent retrieves individually correct facts but fails to assemble the connecting facts that determine which policy or rule actually applies, a failure engineers call the 'flat bag of chunks' problem. 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