Point of View

The LLM Triad: Tune, Prompt, Reward

As language models become increasingly common, it becomes crucial to employ a broad set of strategies and tools in order to fully unlock their potential. Foremost among these strategies is prompt engineering, which involves the careful selection and arrangement of words within a prompt or query in order to guide the model towards producing the…

Vector Database Primer

From the Data Exchange podcast, we present recent conversations with creators of popular vector databases. The popularity of vector databases and vector search has been increasing among technical teams. This is mainly due to the widespread use of dense vector representations of data, made possible by advances in neural networks. Furthermore, the decision of technology…

Navigating the Future of Search

ChatGPT has revived interest in search. It revealed that a blend of artificial intelligence and a prompt-driven interface is exceptionally well-matched for search applications. As a result of ChatGPT, some well-funded search startups are emphasizing their use of large language models (LLM) and launching chatbot-like interfaces. ChatGPT has been receiving a great deal of attention…

Insights from New Data and AI Pegacorns

Meet the Private Companies That Have Reached the $100m Revenue Milestone in the Data Engineering and AI Space. By  Ben Lorica and Kenn So. In 2022, we published our first annual list of data and AI pegacorns – private companies that have reached the $100m revenue milestone. The selection criteria for data pegacorns focused on…

Knowledge Graphs and Foundation Models

Knowledge graphs are not new. I expect usage of knowledge graphs to grow in the coming years as language models and AI applications gain traction. An AI application can benefit from knowledge graphs because they provide a structured, interconnected representation of data. This allows AI algorithms to better understand and make use of the information…

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