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.

It’s not enough to know that an LLM predicts the next token; they want to see if you appreciate the cognitive mechanics inside the model. A strong answer will demonstrate that you know what interpretability means (in general and specifically for language models), why it’s important (debugging models, ensuring ethical behavior, aligning with human values), and some techniques used to interpret models. They also want to see if you’re aware of the concept of reasoning faithfulness—in other words, whether the reasoning a model outputs (like a step-by-step solution or an explanation) genuinely reflects the model’s internal decision process. This second part tests if you understand that large models can sometimes produce answers or explanations that sound right for the wrong reasons.

You’ll need to understand a few core ideas to tackle this interview question confidently. This lesson will explore what interpretability means for language models and why it matters. We’ll use the AI microscope analogy to look inside models, examine whether LLMs plan, and unpack how we can tell if a model’s reasoning is genuine or just a convincing explanation. We’ll also cover how interpretability sheds light on hallucinations, refusals, jailbreaks, and hidden goals, ending with a summary to help you answer clearly in interviews.

What is interpretability in language models?

Interpretability in language models refers to our ability to understand and explain what’s happening inside an LLM when it processes information and produces an output. You can think of a large language model as a very complex function with billions of parameters (weights) that map an input (like a prompt) to an output (the completion). By default, it’s an opaque box—given some input text, it spits out a continuation, and we don’t automatically know why it chose those particular words. Model interpretability is all about shining a light into that opaque box. Interpretability might mean looking at feature importances or simple rules in simpler ML models. But for an LLM, it means digging into things like the activations of neurons, the attention patterns between words, or the representations the model builds internally. The goal is to find human-understandable explanations for the model’s behavior. For example, suppose an LLM suddenly starts talking ...