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How LLMs made their way into the modern data stack in 2023

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Are you ready to bring more awareness to your brand? Consider becoming a sponsor for The AI Impact Tour. Learn more about the opportunities here. When ChatGPT debuted over a year ago, internet users got an always-available AI assistant to chat and work with. It handled their day-to-day tasks, from producing natural language content (like […]
When ChatGPT debuted over a year ago, internet users got an always-available AI assistant to chat and work with. It handled their day-to-day tasks, from producing natural language content (like essays) to reviewing and analyzing complex information. In no time, the meteoric rise of the chatbot drew the world’s attention to the technology sitting at its heart: the GPT series of large language models (LLMs). 
Fast forward to the present day, LLMs – the GPT series and others – are the driving force of not just individual-specific tasks but also massive business operations. Enterprises are leveraging commercial model APIs and open-source offerings to automate repetitive tasks and drive efficiencies across key functions. Imagine conversing with AI to generate ad campaigns for marketing teams or being able to accelerate customer support operations by surfacing the right database at the right time. 
The impact has been profound. However, one area where the role of LLMs isn’t discussed as much is the modern data stack.LLMs transforming the data stack
Data is the key to high-performance large language models. When these models are trained correctly, they can help teams work with their data — whether it is experimenting with it or running complex analytics.
In fact, over the last year, as ChatGPT and competing tools grew, enterprises providing data tooling to businesses looped generative AI in their workflows to make things easier for their customers. The idea was simple: tap the power of language models so the end customers not only get a better experience while handling data but are also able to save time and resources – which would eventually help them focus on other, more pressing tasks.

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