{"id":252576,"date":"2025-04-22T12:15:41","date_gmt":"2025-04-22T10:15:41","guid":{"rendered":"https:\/\/gws-sso-test.graphwise.ai\/use-cases-graph-rag\/"},"modified":"2025-04-22T12:15:41","modified_gmt":"2025-04-22T10:15:41","slug":"use-cases-graph-rag","status":"publish","type":"page","link":"https:\/\/gws-sso-test.graphwise.ai\/de\/use-cases-graph-rag\/","title":{"rendered":"Graph RAG"},"content":{"rendered":"\n

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Home<\/a><\/span> \u00bb Graph RAG<\/span><\/span><\/div>\n\n\n

GraphRAG<\/h1>\n\n\n\n

Eliminate LLM hallucinations and ensure GenAI applications deliver reliable, audit-ready responses<\/h2>\n\n\n\n
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White paper<\/span><\/a><\/div>\n\n\n\n
Leaflet<\/span><\/a><\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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RAG explained<\/a><\/div>\n\n\n\n
About Hallucination<\/a><\/div>\n\n\n\n
Business Potential<\/a><\/div>\n\n\n\n
Resources<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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While LLMs<\/a> offer a massive competitive advantage, they are inherently domain-agnostic and \u201cfrozen\u201d in their training state. For enterprises, this creates a dangerous \u201cHallucination Gap\u201d where the AI generates confident but factually incorrect or nonsensical answers.<\/p>\n\n\n\n
To turn AI into a reliable business tool, you must ground it in your proprietary, real-time data. This is the goal of GraphRAG.<\/p>\n\n\n\n
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What is RAG?<\/h2>\n\n\n\n
Retrieval Augmented Generation<\/strong> (RAG) is a framework designed to make LLMs more reliable by feeding them relevant, up-to-date knowledge extracted directly from a company\u2019s document ecosystem. This context accompanies the user\u2019s question, ensuring the AI bases its response on specific enterprise facts rather than generic training data.<\/p>\n\n\n\n
However, conventional RAG has a significant limitation: it treats your data as a flat list of text chunks. It can find similar words, but it cannot understand the complex relationships<\/strong>, hierarchies<\/strong>, or logic<\/strong> that connect your business information.<\/p>\n<\/div>\n\n\n\n
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What is GraphRAG?<\/h2>\n\n\n\n
GraphRAG<\/strong><\/a> (Graph-enhanced RAG) replaces the flat, \u201cvector-only\u201d approach with an an advanced architecture that uses a Knowledge Graph<\/strong><\/a> to provide a \u201ccontext-infused\u201d retrieval layer.<\/p>\n\n\n\n
By mapping your data into a network of entities and relationships, GraphRAG allows the LLM to navigate your enterprise knowledge like an expert, not a keyword search engine.<\/p>\n\n\n\n
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Your AI Grounded in Enterprise Knowledge (White Paper)<\/span><\/a><\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Relational Intelligence<\/h3><\/div>\n\n\n\n
Instead of treating data as isolated \u201cchunks,\u201d GraphRAG understands hierarchies and dependencies, following your business logic<\/p><\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n
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Multi-Hop Reasoning<\/h3><\/div>\n\n\n\n
GraphRAG connects the dots across multiple disconnected documents simultaneously, answering complex, interconnected questions<\/p><\/div>\n<\/div><\/div>\n<\/div>\n\n\n\n
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Deterministic Grounding<\/h3><\/div>\n\n\n\n
LLMs are forced to follow the logical paths defined by Knowledge Graphs, ensuring every answer is verifiable, auditable, and treaceable<\/p><\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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By 2027, more than 40% of digital workplace operational activities will be performed using management tools that are enhanced by GenAI, dramatically reducing the labor required.<\/p><\/div>\n\n\n\n
Predicts for Generative AI<\/strong><\/p><\/div>\n\n\n\n
Cameron Haight, Chris Matchett<\/p><\/div>\n<\/div>
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Overcome the Limits of Large Language Models<\/h2>\n\n\n\n
While hallucinations can never be completely ruled out, a GraphRAG methodology replaces AI “guessing” with verifiable, graph-based grounding.<\/p>\n<\/div>\n<\/div>\n\n\n\n
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Unlocking the Business Potential: KPIs and ROI<\/h2>\n\n\n\n
The biggest drain on enterprise productivity is the friction of finding and synthesizing information scattered across disparate corporate systems. A GraphRAG framework solves this by connecting your data into a coherent network of knowledge, delivering immediate, measurable returns on efficiency and cost savings.<\/p>\n<\/div>\n<\/div>\n\n\n\n
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Higher Operational Efficiency<\/h3>\n\n\n\n
A typical knowledge worker spends nearly 30% of their day searching for and processing information. Instead of returning long lists of flat documents, a GraphRAG approach<\/strong> connects the dots across disconnected silos to deliver instant, summarized answers.<\/p>\n\n\n\n
Outcome:<\/strong> An increase in operational efficiency of 15-20%<\/strong>.<\/p>\n<\/div>\n\n\n\n
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Faster Onboarding and Democratic Access<\/h3>\n\n\n\n
To get accurate results from standard AI search, employees usually need deep domain expertise (specific jargon and terminology). A GraphRAG architecture<\/strong> bypasses this barrier by mapping out business logic, relationships, and synonyms automatically.<\/p>\n\n\n\n
Outcome:<\/strong> Non-technical or newly onboarded employees can query complex enterprise data accurately from day 1.<\/p>\n<\/div>\n<\/div>\n\n\n\n
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Lower Maintenance Costs<\/h3>\n\n\n\n
Instead of relying on expensive, continuous LLM fine-tuning and massive compute power to improve accuracy, GraphRAG solves the problem at the context layer.<\/p>\n\n\n\n
Outcome:<\/strong> Combining prompt engineering with GraphRAG cuts implementation and maintenance costs by 70%<\/strong>, creating an ROI increase of 3x or higher<\/strong>.<\/p>\n<\/div>\n\n\n\n
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