Dan Selman

Dan Selman unpacks the distinctions among these three concepts critical to meta modeling and offers advice on selecting which to use as a paradigm for your project.

See from experimental results why open text formats work best for training LLMs and the problems AI has with generating complex, structured information.

See how easy it is to implement a method to convert natural language text to nodes and edges in a knowledge graph.

Docusign distinguished engineer Dan Selman runs test queries to detect which tools a large language model (LLM) uses to answer queries against knowledge graph, showing you there's more to it than Retrieval-Augmented Generation (RAG).

See how to combine structured and unstructured semantic queries and to use large language models to orchestrate question answering over a knowledge graph.

See how you can use the “chat with your data” paradigm to convert natural language to graph queries, including using semantics search over vector embeddings.