ai tools want to please us, but their overly-agreeable responses are tweaked to make use happy, not necessarily provide the right, or best, response — don’t trust the response at face value!
Tag Archives: llm
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
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
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