Interview questions Karl-Gustav is ready to dig into. Copy them, tweak them, or bring your own.
1When you integrate retrieval, ranking, and multiple models, what’s the first place latency usually balloons—and how do you fix it?
2You emphasize observability as the “boring superpower.” What does that look like day-to-day for an LLM-powered product?
3Can you share a time when speed and reliability felt like opposites—and how you resolved that tension in practice?
4You’ve said adoption matters more than correctness. What does that principle look like when you’re shipping AI features fast?
5What have you learned about UX patterns that make users actually trust and return to AI products?
6Many startups wrestle with what to ship first—what’s your rule of thumb for choosing a thin slice that still delivers value?
7“Be the brand AI recommends” is a compelling phrase. What does that mean practically for a startup today?
8What are the biggest misconceptions founders have about influencing how LLMs surface their brand?
9How does Attensira help teams move from hoping to be cited to systematically shaping retrieval and grounding?
10In policy or public-sector contexts, how do you balance speed of synthesis with the accuracy and security demands of serious decisions?