Model Interpretability
Explore the fundamentals of model interpretability in large language models. This lesson helps you understand how to analyze internal model activations, attention, and causal interventions. Discover how interpretability tools reveal planning behavior, differentiate genuine reasoning from fabricated explanations, and why these insights are crucial for debugging, safety, and alignment in AI systems.
Interviews for generative AI roles increasingly include questions about model interpretability in large language models (LLMs). A common prompt is: “Explain what model interpretability is in LLMs, and discuss how we can tell whether a model’s reasoning is faithful to its internal computations.” Interviewers use this because it probes two major issues: understanding how complex models operate and determining whether an LLM’s stated reasoning truly reflects what it’s doing under the hood. As models become more powerful and opaque, teams require engineers who can examine their internal behavior and assess their trustworthiness.
This question also reveals whether you’re aware of the field’s current challenges. Everyone knows systems like GPT-5, Claude 4.5, and Gemini 3 are remarkably capable, but do you understand when they’re bluffing, hallucinating, or producing explanations that sound coherent but don’t reflect their actual internal process? Companies rely on people who can diagnose odd behavior, judge whether a chain-of-thought is reliable, and reason about why a model produced a surprising output. Interpretability matters for safety, debugging, and deployment—not just for academic curiosity.
