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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallAn LLM alone cannot answer reliably from information that changes, belongs to your organization, or depends on relationships the model has not been given. Production systems need to retrieve relevant, authorized data, then test whether the answer is correct and fast enough for the task. A knowledge graph can help when those relationships matter, but it is one design option—not a universal requirement or a guarantee of sound reasoning.
What an LLM can—and cannot—know about your business
A language model generates responses from patterns learned during training and the context supplied to it at request time. Its training does not automatically give it access to your latest transactions, internal policies, infrastructure inventory, or private records. For questions that depend on such information, the application must provide relevant context, commonly by retrieving it from an external source and including it in the model’s prompt.
That is the practical point behind Dominik Tomicevic’s June 24, 2025 InfoWorld feature, “LLMs aren’t enough for real-world, real-time projects.” Tomicevic argues for adding a reasoning layer such as a knowledge graph and graph-based retrieval. InfoWorld identifies him as CEO of Memgraph, a graph database company, so readers should understand this as a vendor executive’s design argument rather than a neutral finding that every enterprise LLM needs a graph.
The feature’s questions—whether a transaction looks suspicious, how to respond to a network breach, or what financial risks a business faces next year—are useful illustrations of the need for fresh, domain-specific context. They are scenarios, not reported deployments or measured results.
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What retrieval adds—and what it does not
Retrieval-augmented generation (RAG) searches an external collection for material relevant to a question and supplies selected passages to the model. This can expose the model to proprietary or post-training information without retraining it every time a document changes. Microsoft Learn’s guidance on RAG and indexes describes grounding answers in proprietary content, while emphasizing that quality depends on data preparation, retrieval configuration, and prompts.
Retrieval does not make an answer true merely because the answer cites or reflects retrieved material. If the search returns irrelevant, incomplete, outdated, or conflicting passages, the model can produce an incomplete or inaccurate response. If the application retrieves information a user is not allowed to see, it can expose sensitive data. Retrieved passages also consume context tokens, and the search itself adds work—and therefore potential latency and cost—to the request.
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Microsoft’s groundedness guidance describes ways to check whether responses align with provided sources. That kind of check can help detect unsupported statements, but it is not a substitute for verifying that the sources are authoritative or that the model’s conclusion is correct. Microsoft also describes a faster detection mode for latency-sensitive use and a more explanatory mode; that illustrates a trade-off in a particular tool, not a general performance result for RAG systems.
When a knowledge graph can help
A knowledge graph represents entities and their relationships explicitly: for example, which account belongs to which customer, which device is connected to which network, or which policy applies to which process. That structure can help when a question depends on traversing relationships across records, rather than finding passages that merely contain similar words.
Tomicevic’s examples include connecting accounts for fraud analysis, relating cybersecurity decisions to an organization’s infrastructure, and combining current company information for risk analysis. Graphs may make those connections available to retrieval or graph algorithms, but the examples do not establish that a graph improves every system or that the described deployments were tested.
A 2023 survey by Garima Agrawal, Tharindu Kumarage, Zeyad Alghamdi, and Huan Liu reviews research on using knowledge graphs to augment LLMs, including work aimed at reducing hallucinations and improving reasoning accuracy. It establishes graph augmentation as a research direction, not proof of a guaranteed production benefit. For a workload that does not depend on explicit entity relationships, conventional text retrieval may be simpler and sufficient.
Choose retrieval architecture by workload
Flat text retrieval, graph-based retrieval, and hybrids answer different needs. The sources discussed here do not provide a head-to-head benchmark or identify one universally best design. Compare candidate approaches against the actual questions, data, permissions, and response targets of your application.
| Approach | Best fit to test | Key trade-off |
|---|---|---|
| Flat text RAG | Questions answerable from relevant passages in documents or other text collections. | May miss relationships that are not explicit in the retrieved passages; performance depends on preparation and retrieval configuration. |
| Graph-based retrieval | Questions that require following relationships among entities, such as accounts, assets, or infrastructure components. | Requires representing and maintaining those entities and links; the sources do not establish a general accuracy or latency advantage. |
| Hybrid retrieval | Questions that need both explanatory text and structured connections. | Combines retrieval paths, so relevance, permissions, latency, and cost need to be evaluated across the whole flow. |
Whichever approach you choose, assess source freshness and update processes, relevance of retrieved material, answer correctness, permission enforcement, leakage risk, end-to-end latency and cost, and whether you can observe and repeat evaluations. A graph or RAG label alone does not show whether a system meets its production requirements.
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Evaluate retrieval and answers separately
A system can retrieve the wrong material, retrieve too little, or retrieve appropriate context that the model then misuses. Evaluation should therefore distinguish retrieval behavior from answer behavior and consider the complete application. Microsoft Learn recommends using multiple measures together, including groundedness, completeness, utilization, relevancy, and correctness. These dimensions reveal different failure modes:
- Relevancy: Does the retrieved context address the question?
- Completeness: Does it include enough of the material needed to answer?
- Utilization: Does the response make appropriate use of the retrieved context?
- Groundedness: Are claims in the response supported by that context?
- Correctness: Is the answer itself right, including its reasoning and conclusion?
Groundedness and correctness are not interchangeable: a response can faithfully reflect retrieved text yet draw an incorrect conclusion. Test with representative questions and known outcomes, including cases where relevant information is missing, stale, contradictory, or restricted. Re-run evaluations as documents and user questions change. For agentic RAG, include whether the system selects the right tools, retrieves efficiently, and stays within the end-to-end response target.
Define “real-time” as a measurable requirement
“Real-time” is not a property conferred by using an LLM, a graph, or RAG. It is a requirement for a particular workflow: what information must be current, how quickly it must be available, and how long the user can wait for a response. Retrieval adds processing steps, while graph traversal or additional retrieval paths may add more. The cited guidance does not establish a universal latency figure or comparison among these architectures.
Set a response-time target for the user-facing task, measure the full path from request through retrieval and generation, and weigh that result against answer quality and operating cost. If the target is missed, investigate where time is spent and whether the system can reduce unnecessary retrieval, narrow the context, or use a faster check. Do not claim real-time performance without measurements under the conditions that matter to the deployment.
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The defensible conclusion is narrower than “every LLM needs a graph.” Systems that rely on current or private information need an appropriate way to obtain that information, enforce access rules, and test the resulting answers. Graphs are promising when explicit relationships are central to the task. Retrieval can improve the context available to a model, but it does not remove the need to validate sources, permissions, conclusions, and response performance.
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