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Why you can't simply throw an LLM at your data

Stefanie Dankert
Stefanie Dankert
Head of Content
Why you can't simply throw an LLM at your data

Large Language Models (LLMs) like OpenAI's GPT series are rapidly becoming buzzwords across various industries. These AI-driven tools can synthesize and generate human-like text based on the data they have been trained on. But as tempting as it might be to integrate these technologies directly with your business data, there are crucial steps and considerations that cannot be overlooked.

What is an LLM and how can it benefit businesses?

An LLM, or Large Language Model, is a type of artificial intelligence that processes and generates language-based outputs. These models are trained on a diverse range of internet text. As a result, they can perform a variety of tasks like answering questions, summarizing documents, generating content, and even coding. For businesses, the appeal of LLMs lies in their ability to automate complex tasks that traditionally required human intelligence, potentially saving time and resources while enhancing productivity and innovation.

Combining LLMs with company-internal data and knowledge

Integrating an LLM with company-specific data and internal knowledge bases can tailor its capabilities to more specialized tasks relevant to a particular business or industry. For example, an LLM can be fine-tuned to understand and generate technical reports specific to an industry like pharmaceuticals or to handle customer service inquiries in the context of a company’s product line. This customization allows businesses to leverage the general capabilities of an LLM by applying it in a way that is directly aligned with the specific needs of the business.

There are limitations

While the idea of combining an LLM with internal data is promising, it isn’t as straightforward as it might seem. One common approach to integrating an LLM with specific datasets is using a technique called Retrieval-Augmented Generation (RAG). RAG works by combining a retrieval system that fetches relevant documents from a knowledge base, and a generative system that creates responses based on the retrieved information. However, this method has limitations.

Firstly, the quality of the output heavily depends on the relevance and quality of the information retrieved. If the underlying knowledge base is poorly organized or out-of-date, the answers provided by the LLM can be inaccurate or irrelevant. Unfortunately, this is almost always the case in reality and only small companies are typically able to keep all their internal data and knowledge structured manually.

Secondly, it is not possible to feed the entire company knowledge into the LLM and even if that were possible, the LLM wouldn't be able to distinguish relevant from irrelevant information. To reliably determine relevance one must look at multiple relevance signals such as timeliness, popularity or the collaborators.

Solving it by building your company's knowledge graph

To effectively harness the power of an LLM for your business, it is crucial to engineer a knowledge graph. A knowledge graph organizes and indexes data through relationships and is dynamic in nature, meaning it can evolve with new information. This structure not only helps in better data retrieval but also enables the LLM to understand context more effectively.

Building a comprehensive knowledge graph involves mapping out key data points and relationships relevant to your business. This not only enhances the LLM’s retrieval mechanism by providing it with a rich and well-structured dataset but also improves the accuracy and relevance of the outputs it generates. Without such a framework, businesses risk integrating an LLM that performs below potential, leading to inefficiencies, hallucinations and potentially misinformed decision-making.


While LLMs offer significant advantages, simply throwing one at your data without adequate preparation and infrastructure can lead to suboptimal outcomes. By understanding the inherent limitations and investing in a sophisticated knowledge graph like Zive, businesses can better position themselves to capitalize on the transformative potential of LLMs. This strategic approach ensures that the integration of LLMs not only enhances operational efficiency but also drives sustained innovation and competitive advantage.