My First AI Judge Interview: What Could Possibly Go Wrong?

I entered the HackerRank Orchestrate hackathon with the goal of winning, but found myself unexpectedly facing an AI judge who inquired about the inner workings of Praxi Clew, the financial decision tool I had built. Seeking assistance during the interview, I finished with a score of 66.1 out of 100, gaining a clearer understanding of what needed improvement. For future attempts, I realized that explaining the implementation must be an integral part of building AI-assisted projects.

The AI judge, named Chakra, engaged in an interview-like conversation, asking follow-up questions to delve deeper into the project. The AI Judge's adaptive questioning was impressive, as it built upon previous responses to shape the direction of the conversation. Despite some initial confusion, I maintained that AI played a significant role in the development process.

Throughout the interview, I emphasized the use of AI for extracting facts from messages and images, as well as forecasting balances over 90 days, using Python.

During the interview, I addressed several technical aspects of Praxi Clew, such as the estimation of variable expenses. The implementation used the median of up to five recent amounts in recurring series, which reduced the impact of outliers but had its limitations. I also explained the order of events, where confirmed income was credited before existing expenses were deducted, which in turn affected the payment for proposed purchases.

However, I realized during the interview that my explanations were not fully consistent with the actual implementation, indicating areas for improvement in the future.

In summary, my experience as a first-time AI judge interviewee taught me the importance of clearly explaining the implementation details of an AI-assisted project. While I faced challenges in the interview and had to adapt to the AI judge's questions, I gained valuable insights on where to focus my learning moving forward.