Walking Through an AI or LLM Product You Led to Production Scale

Describe a product you led that applied AI or large language models, at meaningful scale, to a real customer problem. Walk through the problem, your decisions and the results. ```hint Show why AI was the right tool Pick a story where you can explain what the customer struggled with, why a model did better than a simpler rule-based fix, and which decisions only an AI product forced on you, such as how you judged output quality before and after launch. ``` ### Clarifying Questions - Does the product need to be customer-facing, or does an internal tool that served customers indirectly count? - How much technical depth do you want: model choice and evaluation, or mainly product decisions and outcomes? - Does "at scale" mean a large user base, high request volume or a large data set, and is a story that began as a smaller pilot acceptable? ### What a Strong Answer Covers - A concrete customer problem and why AI or an LLM fit it better than rules or a conventional feature. - The candidate's own decisions: scope, data, model or vendor choice, build versus buy, and how quality was defined and measured. - How the product handled AI-specific risks at scale: wrong or invented outputs, privacy of customer data, latency and serving cost. - Measured customer and business impact, with an honest account of what did not work and what changed after launch. - Clear ownership alongside engineering, research or data science, design, legal and privacy partners. ### Follow-up Questions - How did you know the model's output was good enough to ship, and who set that bar? - What would change if the feature had to run on the device, with customer data never leaving the phone? - Tell me about a wrong or harmful output after launch. How did you find it, and what did you change?