Juan Chacon: 2 Year Taller

Little Taller: How has your role evolved over the past year, and what new skills or responsibilities have you taken on?

Juan Chacon:  I’ve taken more responsibilities on our systems health monitoring and process for triaging system alerts, I have now a better understanding of AI fundamentals and its limitations thanks to multiple projects that required research on that area

Little Taller: What project or client engagement from the past year stands out as your favorite, and why did it resonate with you?

Juan Chacon: allir.ai was a big part of my year because it forced me to understand the fundamentals of Artificial Intelligence, research the Transformer architecture and how to leverage an already trained model by adding context to it and generate a human like response

Little Taller: Share a challenge you faced this year and how you overcame it. What did you learn from that experience?

Juan Chacon: I think finding a solution to implement AI for our clients was really hard because there are multiple ways you can do it and most of them are not wrong, the challenge was picking the correct implementation that would give us the most flexibility without too much engineering time from our end, so that translates into a retrieval-augmented generation (RAG) system using our client’s data and objectives to make an AI trained model a useful tool for every specific task

Little Taller: Little Taller places a big emphasis on ownership and orchestration. How have you seen this reflected in your daily work and how has it impacted your work output this year?

Juan Chacon: I think streamlining our process for client system alerts was a good change for me, having a clear path to follow from "something is wrong with this website" to "deploying a hotfix" was a step forward for me on reducing time to fix issues on live websites and servers

Little Taller: Looking ahead, what aspects of your professional life are you most excited about developing further in the coming year?

Juan Chacon: I’d like to continue with more hands on experience with AI implementations, writing the code for our RAG system, deploying it live and getting feedback from users