Model Interpretability

Interviews for generative AI roles increasingly include questions about model interpretability in large language models (LLMs). A common question is: “Explain what model interpretability is in the context of LLMs, and discuss how we can tell whether a model’s reasoning is faithful to its internal computations.” Interviewers love this question because it hits on two hot topics: understanding complex AI models’ thought processes and evaluating if an AI’s explained reasoning matches what’s happening under the hood. In an era of powerful but opaque models, companies care deeply about whether engineers can peek inside the opaque box and ensure models are trustworthy. This question invites you to discuss both what interpretability means for an LLM and how to verify an LLM’s reasoning—a dual challenge that separates merely good candidates from great ones.

Another reason this question is so popular is that it uncovers your awareness of current challenges in AI. Everyone knows that large language models like GPT-4o, Claude 4, or Gemini 2.5 are incredibly capable, but do you know how they work internally, or how to tell when they’re just bluffing or scheming? An interviewer probes if you understand why interpretability matters for safety and reliability. Can you discuss how we try to “open up” an LLM’s brain and inspect its neurons or attention patterns? Are you aware that sometimes an LLM’s step-by-step explanation might sound logical but be misleading? In practice, engineers who build or deploy LLMs must ask, “Why did my model output this weird result?” or “Can I trust this chain-of-thought it generated?” So, interviewers ask about interpretability to see if you’re prepared to handle those real-world concerns, not just generate outputs from a model.