Generative AI heavily relies on data to generate responses to user queries. Large language models (LLMs), such as OpenAI’s GPT-3, are trained on enormous datasets to understand and produce natural language. For instance, GPT-3 was trained using the CommonCrawl dataset, which comprises 570 gigabytes of data and 400 billion tokens. These datasets, although vast, are essentially snapshots frozen in time, and they cannot incorporate real-time information about ongoing events. This limitation can lead to AI-generated responses that are outdated or, in some cases, even incorrect. Moreover, LLMs are susceptible to hallucinations—instances where the AI generates information that appears plausible but is actually false. Even state-of-the-art models, like OpenAI’s, still exhibit hallucination rates ranging from 1.5 to 1.9 percent, according to Vectara’s Hallucination Leaderboard.
This dependency on static, historical data presents two main challenges for companies using LLMs: responses can either be outdated or simply wrong. One way to mitigate these issues is by incorporating real-time data through data streaming. By constantly updating their datasets with fresh information, companies can ensure that their LLMs are working with the most current knowledge. Additionally, utilizing techniques such as retrieval-augmented generation (RAG) allows businesses to integrate specific business data into generative AI models, providing a more accurate, timely, and tailored response to user queries.
RAG addresses these challenges by creating a dynamic, searchable dataset composed of vector representations of information. When a user submits a query, the system searches through these vectors to find relevant semantic matches, which are then used by the LLM to generate a response. The beauty of RAG lies in its ability to continuously update the dataset, ensuring that new or additional information can be incorporated as needed. This keeps the LLM’s responses aligned with the latest available knowledge, reducing the likelihood of outdated or irrelevant answers.
Despite the benefits, RAG also faces its own set of challenges. One of the primary issues arises when multiple documents contain similar or identical information. As these documents are broken into smaller chunks and converted into vector embeddings, it becomes difficult for the system to identify the most relevant information when chunks are too alike. Additionally, RAG struggles when a query requires information that spans across multiple documents that are interrelated or cross-referenced. The system does not inherently understand the relationships between these documents, which can result in inaccurate or incomplete responses. For example, consider a chatbot that uses RAG to answer customer inquiries about a product catalog. If the catalog contains highly similar product listings or cross-references to other documents, RAG may struggle to deliver the most accurate response, especially if the query involves multiple interlinked pieces of information.
To overcome these limitations, a more robust knowledge management approach is needed—one that complements RAG’s capabilities. Research from Microsoft has explored combining RAG with knowledge graphs in a technique called GraphRAG. This approach seeks to enhance RAG by leveraging the structured, relational data in knowledge graphs, enabling the AI to better understand connections between documents and deliver more accurate, contextually aware responses. By integrating the strengths of both RAG and knowledge graphs, businesses can ensure more precise and reliable outcomes from their generative AI systems, ultimately improving user experience and decision-making.