Common misconceptions about Large Language Models

How LLMs Actually Fail: Token-Level Failure Modes Behind Confident Output

LLMs don't fail randomly — they fail in predictable, structural ways rooted in how they generate text token by token. Understanding the token-level mechanics of hallucination, sycophancy, and instruction drift lets you design prompts and systems that route around these failure modes.

What Are Common Misconceptions About LLMs?

Large language models generate confident-sounding text, which makes it easy to form wrong mental models about what they actually do. Understanding what LLMs are not — not databases, not reasoning engines, not search — is as important as understanding what they are for using them effectively.