When undertaking my research into all the various EV options I could have done so manually, checking various websites for the information I sought, but as a techie I don’t know I could look myself in the mirror if I didn’t take advantage of the latest genAI tools like Gemini.
Below is a prompt I used with Gemini in December when first considering EVs:
I am considering a novated lease for an electronic vehicle. I live in rural New South Wales and would drive roughly 10,000kms per year, though a majority of my driving will be around town with occasional long distance driving of 500-750 kilometres. I would like to compile detailed information, including reviews, warranty information, car specifications, public charger availability, top 5 positive and negative points from australian car review sites, and whether the manufacturer includes servicing and roadside assistance as standard. I am interested in the following cars: Ford Mustang Mach-E Premium, Polestar 4 Long Range Single Motor, Tesla Model Y Long Range, Volkswagen ID.4 Pro, Volkswagen ID.5 Pro, Kia EV5 GT-Line
The data returned by Gemini I was then able to export to Sheets and further manipulate to suit my needs. The speed with which I could gather this information compared to gathering manually can’t be overlooked, later versions of Sheets added genAI features that further enhanced this. Before finally making my decision I would refer often to this spreadsheet and expand with leasing and tax-related information I collated manually.
EV Journey 
Leave a comment