Unlocking AI Trust: RAG and GraphRAG

AI’s lightning-fast evolution, accelerated by Large Language Models (LLMs), is turning heads worldwide, promising to shake up business, knowledge, and data management. To get the inside scoop, Unisphere Research surveyed 382 executives in December 2024, diving into LLM and RAG adoption, applications, and hurdles. Their study, “State of Play on LLM and RAG: Preparing Your Knowledge Organization for Generative AI,” reveals how companies are using these tools to boost knowledge strategies and tackle LLM limitations. By focusing on knowledge management leaders, the survey captures real-world experiences and strategic thinking from the AI front lines, offering key insights into early LLM adoption and concerns.

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AI Everywhere: 85% of Execs Report LLM Initiatives

According to the survey, generative AI (GenAI) and LLMs have quickly become an integral part of most organizations. The majority of executives (85%) reported that their organizations are either actively testing or partially deploying LLMs, indicating a widespread recognition of their potential to enhance knowledge delivery across enterprises.

These efforts are particularly focused on areas such as content creation, content customization, customer self-service, knowledge discovery, knowledge management, intelligent search, and assisting customer service staff. This highlights the diverse range of applications where organizations are seeking to leverage the power of LLMs to improve efficiency and knowledge discovery and accessibility.

AI’s Double Edge: Enthusiasm Meets Security Concerns

While enthusiasm for LLMs is high, the survey indicates that AI initiatives are still in the early stages of maturity, primarily within testing and development phases. A significant consensus emerged regarding the indispensable role of human oversight in mitigating these potential pitfalls.

Data quality stands out as the foremost concern among organizations implementing generative AI and LLMs, cited by 71% of respondents, followed closely by data security and privacy considerations. Even among organizations with LLMs already in production, an overwhelming 89% agree on the importance of human involvement to some degree.

Notably, a substantial majority of respondents, 71%, perceive the increasing use of GenAI as carrying security and quality risks. This concern is particularly pronounced within the government and education sectors, where respondents are three times more likely to express significant risk exposure compared to their counterparts in the technology sector. This apprehension is reflected in the lower adoption rates of large language models within government and education organizations, standing at 7% compared to the overall survey average of 27%. As far as adoption of LLM-based approaches, technology-focused companies lead the way followed by financial services, manufacturing and pharmaceuticals.

Survey Says: GraphRAG is Key to Overcoming AI Obstacles

To address these concerns, nearly one-third of LLM users are turning to Retrieval-Augmented Generation (RAG) as a crucial link between proprietary and domain specific data within corporate databases and LLMs. Close to half of the respondents believe that RAG – which is a technique that enhances the performance of AI models by connecting them to external knowledge sources thus improving the accuracy and relevance of responses from LLMs – will play a vital role in making information more actionable and closer to real time. This highlights the growing recognition of RAG as a key technology for grounding LLM outputs in reliable and contextually relevant data, thereby enhancing their accuracy and trustworthiness.

In particular, the report highlights GraphRAG as a variant that enhances traditional RAG methods by leveraging the power of knowledge graphs. The survey data reveals a strong emphasis on specific benefits that companies anticipate from GraphRAG, with improved contextual results and more actionable data topping the list of priorities. These expectations highlight a desire for AI solutions that move beyond simple information retrieval to provide deeper understanding and drive tangible business value.

Instead of treating organizational knowledge as isolated documents, GraphRAG arranges data into a network of interconnected entities and relationships. This structured approach goes beyond simple keyword or vector searches and enables a better understanding of context and semantic relationships. As a result, GraphRAG offers key benefits: improved contextual awareness, more accurate answers, reduced time to insights, enhanced user trust, and the ability to perform multi-hop reasoning (linking seemingly unrelated information and surfacing latent relationships across domain entities) with increased transparency in the AI’s reasoning. The survey confirms and emphasizes that “modern approaches such as knowledge graphs” are crucial for leveraging both multi-modal – structured and unstructured – data to build robust enterprise solutions with RAG systems.

Building Reliable AI with RAG, Knowledge Graphs, and Trust

Notably, 59% of respondents with productive LLMs say they use knowledge graphs with their RAG technologies. This suggests that knowledge graphs play a critical role in overcoming the typical obstacles and risks associated with the success of generative AI initiatives. It also underscores the growing importance and potential of GraphRAG in the evolving landscape of LLM applications.

The survey also emphasizes the transformative potential of LLMs and RAG systems for organizations seeking to unlock the wealth of untapped knowledge within their unstructured data. This is not necessarily surprising, as these technologies have long been credited for bringing valuable insights to the surface, enhancing knowledge delivery, and improving efficiency across various business functions. LLMs and RAG systems can unlock valuable insights from unstructured data, but organizations must shift to a data-centric approach and prioritize data quality.

Human oversight remains essential for quality assurance and training. Knowledge graph infrastructure and semantic AI are crucial for reliable AI adoption. Explainable AI is also becoming a necessity due to legislation and public acceptance.

As the survey demonstrated, organizations looking to maximize their AI investments should focus on data quality, human curation, and strategic integration of advanced RAG techniques like GraphRAG. These modern approaches provide a critical way for organizations to leverage their structured and unstructured data, bridge the gap between corporate databases and LLMs and remove the typical barriers and risks to generative AI success.

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