  Before applying data analytics or machine learning to a data set, a vital
step is usually the construction of an informative set of features from the
data. In this paper, we present SMARTFEAT, an efficient automated feature
engineering tool to assist data users, even non-experts, in constructing useful
features. Leveraging the power of Foundation Models (FMs), our approach enables
the creation of new features from the data, based on contextual information and
open-world knowledge. Our method incorporates an intelligent operator selector
that discerns a subset of operators, effectively avoiding exhaustive
combinations of original features, as is typically observed in traditional
automated feature engineering tools. Moreover, we address the limitations of
performing data tasks through row-level interactions with FMs, which could lead
to significant delays and costs due to excessive API calls. We introduce a
function generator that facilitates the acquisition of efficient data
transformations, such as dataframe built-in methods or lambda functions,
ensuring the applicability of SMARTFEAT to generate new features for large
datasets. Code repo with prompt details and datasets:
(https://github.com/niceIrene/SMARTFEAT).
