Cookie Consent

I use cookies to understand how my website is used. This data is collected and processed directly by me, not shared with any third parties, and helps us improve our services. See my privacy and cookie policies for more details.

PartyRock: Madonna Song-To-Tour Finder

New York | Published in AI | 2 minute watch |     
Madonna closing The Celebration Tour on 25 October 2023 at The Royal Arena, Copenhagen

Given Adam’s announcement yesterday about Amazon PartyRock powered by Amazon Bedrock, I decided I couldn’t wait to get my hands on it and see what kind of app I could build.

Well, if you know me, you’ll know I saw Madonna three times in 2023 on her The Celebration Tour, and the last show I went to in Copenhagen was the 51st time I’ve seen her in concert since 1990. So you could say, I’m just a little bit of a fan!

Well the old brain cells aren’t what they used to be, so I used PartyRock to build the Madonna Song-To-Tour Finder!! This two minute video shows just how quickly you can build apps with PartyRock and most importantly, use PartyRock to learn how to write great prompts to feed into a generative AI foundation model. Have a play with the app (or just build your own!).

The Prompt

Here’s the prompt I used to generate the app:

I'm a huge Madonna fan, but I can never remember which of her songs are sung on each tour she has done. So, l'd like an app which allows me to enter a Madonna song name, and then for the app to list the tours she is known to have sung that song on.

It’s a fairly simple prompt, asking PartyRock to provide a single input field and then to list the tours after the prompt has been completed.

The Result

About the Author

Mario Thomas is a Chartered Director and Fellow of the Institute of Directors (IoD) with nearly three decades bridging software engineering, entrepreneurial leadership, and enterprise transformation. As Head of Applied AI & Emerging Technology Strategy at Amazon Web Services (AWS), he defines how AWS equips its global field organisation and clients to accelerate AI adoption and prepare for continuous technological disruption.

An alumnus of the London School of Economics and guest lecturer on the LSE Data Science & AI for Executives programme, Mario partners with Boards and executive teams to build the knowledge, skills, and behaviours needed to scale advanced technologies responsibly. His independently authored frameworks — including the AI Stages of Adoption (AISA), Five Pillars of AI Capability, and Well-Advised — are adopted internationally in enterprise engagements and cited by professional bodies advancing responsible AI adoption, including the IoD.

Mario's work has enabled organisations to move AI from experimentation to enterprise-scale impact, generating measurable business value through systematic governance and strategic adoption of AI, data, and cloud technologies.