Tag Archives: AI

yarp: yet another rag post (this time using sql)

you don’t have to know python or bother your data scientists to start exploring genai concepts like rag; you just need a tool that offers these features in a familiar sql interface

unstructured docs in ai (the wild west)

rag ai apps can only be as good as the parsed and chunked data that fuels them – testing, testing, and more testing the outputs of all the various available libraries with the front-end apps is critical

optionality and common sense (why i returned to starburst)

i’m so excited to have returned to starburst and be focused on rebooting the devrel function, not to mention staying active in the trino and iceberg communities — long live the icehouse

the effect of ai on intelligence (behold the idiocracy)

the long-term benefits of sunscreen have been proved by scientists whereas my advice on ai has no basis more reliable than my own meandering experience; i will dispense this advice now, but trust me on the sunscreen

develop, deploy, execute & monitor in one tool (welcome to apache nifi)

for those not familiar with apache nifi, come on a short overview of how this framework rather uniquely spans so many of the phases of the typical software development lifecycle

exploring ai data pipelines (hands-on with datavolo)

after explaining what rag ai apps are all about & showing what a typical ai data engineering pipeline looks like, i wanted to offer a hands-on lab exercise actually building a simple pipeline use datavolo cloud

understanding rag ai apps (and the pipelines that feed them)

i’m learning all about rag ai apps and wanted to try to explain, at a high-level, what these are all about plus do the same for the etl pipelines that are key to their success