The highly general methodology for holistic optimisation shown herein can be applied in any chemistry laboratory to address the inability to complete multi-variable multi-metric optimisation in many fields beyond photocatalytic CO2 reduction (incl. materials, polymer and organic synthesis). While the approach is demonstrated with CO2 reduction, a clear example application is optimising organic synthesis reactions so they are high yielding, fast and general (wide substrate scope), i.e., multi-variable multi-metric reaction optimisation is required. For an organic reaction, post-optimisation machine learning would then quantify the importance of each variable to each metric and guide mechanistic understanding; e.g., the importance of the buffer herein is analogous to additives that unexpectedly enhance organic reactions, which has been the focus of a prominent high-throughput study. 6