In this note, we revisit the envelope dimension reduction, which was first introduced for estimating a sufficient dimension reduction subspace without inverting the sample covariance. Motivated by the recent developments in envelope methods and algorithms, we refresh the envelope inverse regression as a flexible alternative to the existing inverse regression methods in dimension reduction. We discuss the versatility of the envelope approach and demonstrate the advantages of the envelope dimension reduction through simulation studies.