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Description
While there are multiple ways of leveraging Knowledge Graphs (KG) for Retrieval Augmented Generation (RAG), here we illustrate a significant example of how Knowledge Graphs fit in an AI query pipeline, using a Knowledge Graph to enrich a Large Language Model (LLM) prompt for extracting relevant domain information from documents. We explain this scenario in the context of a specific business use-case that deals with equipment industry standards.
We build on the foundation of the Graph-based RAG KG Creation Best Practice where we created a Knowledge Graph, and now we show how to use the Knowledge Graph to answer practical business questions using a simple and repeatable workflow.
Expected Outcome
By following this guide, you can setup a simple process where business facts are found in documents and traced back to their definitions in your knowledge domain. This can improve the accuracy of your AI results and help you explain where each answer comes from.
Key Concepts
In addition to the foundational concepts that we introduced in the Graph-based RAG KG Creation Best Practice, let’s first introduce some additional concepts that are critical to understand the remaining sections of this article.
Pipeline Integration: The process of connecting your KG to other AI components (like LLMs) so you can use facts from the graph as part of a bigger workflow. For example, the KG gives expected object properties to the LLM before extraction.
Prompt Templating: Building a structured prompt that includes terms or properties from your KG. This lets you control what the LLM looks for when reading documents.
Business Traceability: The ability to show where every answer comes from, by linking LLM outputs back to facts in your KG. This is important for audits and trust. Read More SAP Developers
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