Maybe I'm missing something, but aren't LLMs for Data Engineers a bit overkill? Wouldn't it be more practical to focus on building experience in real-world projects?
This post is targeted for scenarios where Data Engineers end up going through AI implementations since they are the closest technical position to AI in many companie with small teams. .
Structured Outputs is one of the most challenging real world use cases since it can feed frontend and backend processes that need consistent outputs.
Maybe the title suggested something different and that's where the confusion lies? What do you think?
Maybe I'm missing something, but aren't LLMs for Data Engineers a bit overkill? Wouldn't it be more practical to focus on building experience in real-world projects?
This post is targeted for scenarios where Data Engineers end up going through AI implementations since they are the closest technical position to AI in many companie with small teams. .
Structured Outputs is one of the most challenging real world use cases since it can feed frontend and backend processes that need consistent outputs.
Maybe the title suggested something different and that's where the confusion lies? What do you think?