Use cases overview

The use cases in this section are built on knowledge graph (KG) technologies for 20+ years in anticipation of generative AI (GenAI) large language model (LLM) Retrieval Augmented Generation (RAG) prompt engineering and embeddings. The root of today’s current technology advances began with natural language processing (NLP) and the lack of algorithms and processing approaches required to make AI useful.

Technology innovation and processing power and costs have made it possible for the AI field to mature. We are ready for the a-ha moment where human productivity takes a great leap forward with AI assistance! The beginning of that process is happening now with LLMs and advanced approaches to tuning those LLMs with prompt engineering. You can muster data architectures and data engineering solutions to exploit the opportunity via DataOps and MLOps (or AIOps if you are working with GenAI models). Working with key partners that have knowledge graph capabilities...