358: The limitations of AI models in understanding context (With Jonathan Macoskey)

Episode notes

Dave Rubinstein and Jonathan Macoskey discuss the limitations of current AI models in understanding context, particularly in enterprise settings. Macoskey explains that while language models have improved with internet connectivity, they still struggle with outdated information and proprietary data. He highlights Lovelace's context engine, which integrates data from various sources into a knowledge graph, addressing issues like entity resolution and recall. This system ensures AI agents can provide accurate, cited information, enhancing trust and decision-making. The conversation also touches on the challenges of scaling large language models and the importance of grounding AI responses in reliable data.

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