Из заметки Inside OpenAI’s in-house data agent
OpenAI’s data platform serves more than 3.5k internal users working across Engineering, Product, and Research, spanning over 600 petabytes of data across 70k datasets. At that size, simply finding the right table can be one of the most time-consuming parts of doing analysis.
Первая мысли, может им лучше свою дата модель перестроить? Там глядишь и жить станет проще и пара сотен лишних петабайт освободится
Люди, которые так умеют, автоматически попадают в топ 10%
Из интересного - описан структурный подход к получению контекста/меты
OpenAI’s data platform serves more than 3.5k internal users working across Engineering, Product, and Research, spanning over 600 petabytes of data across 70k datasets. At that size, simply finding the right table can be one of the most time-consuming parts of doing analysis.
As one internal user put it:
“We have a lot of tables that are fairly similar, and I spend tons of time trying to figure out how they’re different and which to use. Some include logged-out users, some don’t. Some have overlapping fields; it’s hard to tell what is what.”
Первая мысли, может им лучше свою дата модель перестроить? Там глядишь и жить станет проще и пара сотен лишних петабайт освободится
One of the agent’s superpowers is how it reasons through problems. Rather than following a fixed script, the agent evaluates its own progress. If an intermediate result looks wrong (e.g., if it has zero rows due to an incorrect join or filter), the agent investigates what went wrong, adjusts its approach, and tries again.
Люди, которые так умеют, автоматически попадают в топ 10%
Из интересного - описан структурный подход к получению контекста/меты