Enable Better Generative AI
DataWalk’s unique knowledge graph grounds LLMs
Many organizations want to take advantage of the benefits of Large Language Models (LLMs), but are concerned about the accuracy of results and the complexity of incorporating their organization’s domain knowledge. DataWalk software enables you to leverage the power of LLMs with trusted responses and simplified no-code deployment, enabling you to unleash the full potential of generative AI for your organization.
The Challenge: Getting Results Your Organization Can Trust
LLMs are great for general knowledge, understanding language, and summarizing content. However, they are not trained in domain-specific knowledge or your organization’s internal content, which are critical to providing relevant answers that you can trust.DataWalk Supplements Your LLM
DataWalk’s graph AI platform can augment your LLM with your organization’s structured and unstructured data, so the LLM can reliably answer even internal organization-specific questions. You can effectively ground (using RAG, or Retrieval Augmented Generation) either a commercial LLM such as GPT-4 via API, or an open source LLM such as Llama.Improving LLM Accuracy
DataWalk incorporates your organization’s knowledge with valuable context and relationship information so that the LLM summary is grounded in facts. The responses are more relevant, accurate and trusted and can include links to the documents upon which the summary response was generated. DataWalk can act as an agent, breaking down a question into appropriately sized tasks and sequencing them to the LLM via the DataWalk API.No Coding Required!
With DataWalk you can simplify LLM deployment as there’s no need to write code to coordinate between your company’s information and your chosen LLM.
Unlike many other grounding options which require you to write the agent code coordinating the calls and tasks between different system components, DataWalk has this capability built-in for many LLMs including ChatGPT and Llama.