{"id":32,"date":"2026-05-13T06:30:00","date_gmt":"2026-05-13T06:30:00","guid":{"rendered":"https:\/\/regentis.ai\/blog\/?p=32"},"modified":"2026-09-07T12:46:05","modified_gmt":"2026-09-07T12:46:05","slug":"beyond-rag-graphrag-hybrid-retrieval-and-the-red-threads-in-company-data","status":"publish","type":"post","link":"https:\/\/regentis.ai\/blog\/beyond-rag-graphrag-hybrid-retrieval-and-the-red-threads-in-company-data\/","title":{"rendered":"Beyond RAG: GraphRAG, Hybrid Retrieval, and the Red Threads in Company Data"},"content":{"rendered":"\n
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In my previous article<\/a>, I wrote about why classical RAG is a strong starting point for connecting company knowledge to AI systems, but why it may not always be enough on its own. Before reading this article, if you have not read the previous one, I would like to direct you to it first.<\/p>\n\n\n\n

Classical RAG<\/a> often works with a \u201cchunk, embed, retrieve\u201d logic. In other words, it divides documents into small parts, turns these parts into vectors, and brings the ones that are geometrically closest to the question in front of the model.<\/p>\n\n\n\n

In many scenarios this setup performs well, since it keeps the model from relying solely on its own training data and gives it access to up-to-date information within the company. Yet artificial intelligence is more than a search engine; it is a synthesizer. So we expect it not just to locate the right document, but also to grasp which decisions, processes, teams, and results that document is connected to.<\/p>\n\n\n\n

Where classical RAG struggles: multi-hop reasoning
<\/h2>\n\n\n\n

This is where classical RAG starts to struggle. Because classical RAG cannot always make strong logical inferences that require multiple steps, also called multi-hop reasoning. It can find a piece of information, but it can struggle to understand how that information is connected to another piece of information, which decision affected the result, or which previous problem an event is related to.<\/p>\n\n\n\n

In Microsoft Research\u2019s study \u201cFrom Local to Global: A Graph RAG Approach to Query-Focused Summarization,\u201d it is also stated that classical RAG can be insufficient, especially for more general questions that spread across the whole dataset and require context. In the study, questions such as \u201cWhat are the main themes in the dataset?\u201d are given as examples. These questions require understanding the whole collection, not just finding one piece.<\/p>\n\n\n\n

This shows us that in some questions, the issue is not only \u201cbring the closest piece.\u201d Sometimes, the answer requires many pieces to be brought together, connected, and summarized. When this context is not built, the system cannot always notice its own mistake. In other words, the self-correction mechanism is not strong enough.<\/p>\n\n\n\n

When the model draws a wrong conclusion, there is no built-in control mechanism that can catch this and say, \u201cI made the wrong connection here.\u201d<\/p>\n\n\n\n

For this reason, one of the biggest risks in AI systems today, as we all know, is hallucination.<\/p>\n\n\n\n

What is GraphRAG, and how is it different?
<\/h2>\n\n\n\n

Because of this, new approaches beyond classical RAG are being evaluated. One of these approaches is GraphRAG.<\/p>\n\n\n\n

Entities and relationships sit at the heart of GraphRAG. That means information is no longer treated merely as numbers, points, or fragments that sit near or far from one another in vector space. Instead, the system attempts to build a clearer map of who is linked to whom, what led to what, and which decision influenced which project.<\/p>\n\n\n\n

In Microsoft\u2019s GraphRAG documentation, this structure is explained as extracting a knowledge graph from raw text, creating community hierarchies on this graph, and producing summaries for these communities. So, instead of the flat text pieces in classical RAG, a more structured and hierarchical information representation is being built. For this reason, it would be incomplete to see GraphRAG only as a more advanced search method.<\/p>\n\n\n\n

Actually, the aim here is to move information into a more relational structure.<\/p>\n\n\n\n

For example, classical RAG can find a document. But GraphRAG also tries to consider which customer request, technical decision, incident, or team metric that document may be related to.<\/p>\n\n\n\n

In a survey study published in 2025 about GraphRAG, it is also stated that this approach differs from classical RAG in three main ways: it represents information with a graph structure, it clearly captures entity relationships and domain hierarchies, and it better supports the need for multi-hop reasoning with graph-based retrieval.<\/p>\n\n\n\n

The detective board: GraphRAG draws the red strings
<\/h2>\n\n\n\n

In my previous article, I used the example of a detective board while explaining classical RAG.<\/p>\n\n\n\n

We pictured a cork board holding photos of suspects, crime scenes, dates, and documents. Detectives pin these photos onto the board. Then they mark the connections between them with red strings.<\/p>\n\n\n\n

Classical RAG can find and bring the related photos on this board.<\/p>\n\n\n\n

But GraphRAG tries to build the red strings between those photos too.<\/p>\n\n\n\n

So it does not only say, \u201cthis document is relevant.\u201d It tries to create a relationship ground that can say, \u201cthis document may be connected to this person, this decision, this event, and this result.\u201d<\/p>\n\n\n\n

Why ontology matters in company data
<\/h2>\n\n\n\n

At this point, another concept is discussed as a risk: ontology.<\/p>\n\n\n\n

Ontology helps the system define concepts more clearly. In other words, it makes topics such as what a concept means, which type an entity belongs to, and what a relationship means more controlled. This reduces concept confusion. Because inside a company, the same concept can have different meanings for different teams. For one team, \u201cincident\u201d may mean a technical outage, while for another team it may mean an operational problem on the customer side. Or concepts such as \u201cproject,\u201d \u201cinitiative,\u201d \u201ctask,\u201d and \u201cepic\u201d may be stored in different ways in different tools.<\/p>\n\n\n\n

When these concepts are not clearly modeled, the system can end up drawing the wrong relationship even after finding the right pieces.<\/p>\n\n\n\n

In a 2025 ontology-aware GraphRAG study, it is also stated that a structured ontology-aware graph approach is important for producing more reliable question-answering results. It gives strong results especially in generation quality metrics such as faithfulness, coherence, and answer score.<\/p>\n\n\n\n

Where GraphRAG still falls short
<\/h2>\n\n\n\n

GraphRAG offers a strong approach at this point. For this reason, it reduces this risk, but still does not completely solve it. Because our queries may not always be very clean, meaningful, and written in natural language.<\/p>\n\n\n\n

At times we may search using numerical codes, internal company abbreviations, or phrases detached from any context. These queries make sense to us because we know what they mean inside the company. But once written down as text, they may not be clear enough for the system.<\/p>\n\n\n\n

Also, GraphRAG has its own operational challenges. Creating a knowledge graph, extracting entities and relationships, doing graph traversal, and running this with low latency in a production environment is not easy. In a 2025 study on SAP datasets, it is stated that GraphRAG is promising for multi-hop reasoning and structured retrieval in enterprise environments. However, the cost of creating a knowledge graph and the latency of graph-based retrieval are important barriers for wider use. In the same study, with a lighter graph construction approach, up to 15% improvement is reported in some metrics compared to traditional RAG.<\/p>\n\n\n\n

Hybrid retrieval: vector, keyword, metadata, and graph together
<\/h2>\n\n\n\n

For this reason, in enterprise AI systems, instead of trusting only one retrieval approach, hybrid retrieval approaches are becoming more important. Hybrid retrieval became the enterprise industry standard in 2026.<\/p>\n\n\n\n

Hybrid retrieval aims to bring different search and context-building methods together in one system.<\/p>\n\n\n\n

It starts with vector search, which tries to capture the meaning depth of the query. Then, traditional lexical algorithms such as BM25 are used to consider exact word matches and term importance. Metadata filtering narrows the results with fields such as date, team, project, source, or permission. GraphRAG tries to include the red strings in the system by following relationships.<\/p>\n\n\n\n

In Redis\u2019s 2026 article about hybrid search, this structure is explained as an approach that combines keyword precision with semantic understanding. It is especially emphasized that in areas such as technical documents, law, and healthcare, both exact term matching and semantic closeness are needed.<\/p>\n\n\n\n

In another 2025 study, Vector RAG, GraphRAG, and Hybrid GraphRAG are compared on ORAN documents. According to the results, GraphRAG increases the context relevance metric by 11%, while Hybrid GraphRAG provides an 8% improvement in factual correctness. These rates should not be accepted as general rules for every dataset, but they are important because they show why this approach is taken seriously.<\/p>\n\n\n\n

The real question is not RAG or GraphRAG
<\/h2>\n\n\n\n

It would not be realistic to make this whole structure meaningful with only one search method. That is why, in my last two articles, I did not try to answer the question \u201cRAG or GraphRAG?\u201d I tried to answer the question of how we can model company knowledge in a way that AI can really use.<\/p>\n\n\n\n

Because on the enterprise AI side, the value is not only in accessing information. The real value is in understanding which concepts, decisions, processes, and results that information is related to.<\/p>\n\n\n\n

Classical RAG is a strong starting point in this journey. GraphRAG tries to make the relational side of this information more visible. Hybrid retrieval aims to build a more balanced and more reliable structure by bringing different search methods together.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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