  We present a computationally efficient galaxy archetype-based redshift
estimation and spectral classification method for the Dark Energy Survey
Instrument (DESI) survey. The DESI survey currently relies on a redshift fitter
and spectral classifier using a linear combination of PCA-derived templates,
which is very efficient in processing large volumes of DESI spectra within a
short time frame. However, this method occasionally yields unphysical model
fits for galaxies and fails to adequately absorb calibration errors that may
still be occasionally visible in the reduced spectra. Our proposed approach
improves upon this existing method by refitting the spectra with carefully
generated physical galaxy archetypes combined with additional terms designed to
absorb data reduction defects and provide more physical models to the DESI
spectra. We test our method on an extensive dataset derived from the survey
validation (SV) and Year 1 (Y1) data of DESI. Our findings indicate that the
new method delivers marginally better redshift success for SV tiles while
reducing catastrophic redshift failure by $10-30\%$. At the same time, results
from millions of targets from the main survey show that our model has
relatively higher redshift success and purity rates ($0.5-0.8\%$ higher) for
galaxy targets while having similar success for QSOs. These improvements also
demonstrate that the main DESI redshift pipeline is generally robust.
Additionally, it reduces the false positive redshift estimation by $5-40\%$ for
sky fibers. We also discuss the generic nature of our method and how it can be
extended to other large spectroscopic surveys, along with possible future
improvements.
