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Generative AI Guidebook


Getting started with knowledge graph-infused LLMs


Generative AI has been a major “disrupter” in the global market, shifting the way that people use technology at the workplace and in their private life. A study conducted by Salesforce at the end of 2023 found that 75 percent of early adopters of the technology (those who began to use it regularly in their personal lives) were looking to use it in the workplace to automate tasks. However, 60 percent of skeptical users said they would feel more comfortable with GenAI in the workplace if they could trust the data being used and the highest factor for them to be on board with Generative AI is if the technology could have human supervision.

Large language models (LLMs) are a type of GenAI that are admittedly powerful but have a strong deficit in traceability and accuracy – which are two qualities an organization cannot skip out on. This Generative AI guidebook aims to address the reservations that non-users have and offer up another alternative for organizations: graph-based retrieval augmented generation, or Graph RAG for short.

A Graph RAG design pattern combines symbolic AI (knowledge graphs) and generative AI (LLM) for better domain fidelity and fewer hallucinations. It uses natural language processing and semantic search to ensure that documents are found based on the user’s intent of the query, as well as generates new content and summaries about this topic while in dialogue with the chatbot.

This Generative AI guide will give run you through:

  • A practical use case of graph-infused LLMs
  • The major limitations of GenAI
  • Why graph databases are better than vector databases
  • Business outcomes for early adopters of Graph RAG
  • How to get started plus a information about our Starter Kit

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