😄 Funny Moments:
During the day, amidst all the intense hacking, one team stood out with their humour during an energizer. They got disqualified in the paper-airplane-race by a hilarious attempted to throw a ball of paper with an apple inside of it. The laughter echoed through the room.
🏁 The Grand Finale🥇
As the hackathon reached its climax, all the teams gathered to present their ideas and demos to the jury. The competition was fierce, with a neck-to-neck finish between the top two teams. The suspense was intense, and in the end, the team that had been able to automate titles emerged victorious. The difference between the top two was so close that there was a surprise in store for the runner-up as well. Both teams were granted a golden opportunity to visit the Google office in Amsterdam. Not only that, but they also get the chance to develop their Proof of Concept (PoC) into a Minimum Viable Product (MVP) with the support of Google!
The results? # AI Title Generatation POC
With the current generation of large language models (LLMs), the most successful implementations are designed to work with people rather than replace them. In fact, when implemented correctly, it can actually give the user a more human-like and curated experience with a product.This is the experience our title generation hackathon concept aimed to create. The problem the retailers are commonly faced with is, we give them a paragraph of criteria for their product titles, and then give them a text box and expect them to come up with what they think is the best title. Given this problem, how could we make the experience better? Rather than provide the retailer with more walls of text, we can instead use a LLM to take all we know about good titles, and generate candidate product titles for the retailer to choose or learn from.Given we wanted to implement this, we needed to know where to start, and as we learned at the beginning of the hackathon, this is done by picking out a model (Google’s Bison) and then starting on the prompt engineering. What is prompt engineering?
After enumerating these rules, we proceeded to provide 5 high-quality titles from the same “Product Chunk” in the Product Catalog as the product we were trying to generate a title for. Finally, we provideda description of the product. We then submitted this prompt 5 times to hopefully generate 5 possible titles. After generating the titles, we provided the user with the generated candidate titles and allowed them to select which title they liked best.
As for future improvements, the most likely next step is to improve performance. Largely this would just come down to pre-generating the titles in a daily batch process. This would also allow us to apply additional filtering on candidate titles based on an objective scoring of the titles.
