Key technical breakdown of how LLMs generate responses for AI interviews

An article outlines the five core components candidates should explain when asked how large language models generate responses during AI engineering interviews. The essential steps include tokenization, embedding creation, attention mechanisms, token generation through sampling, and understanding hallucination root causes. Interviewers frequently note that candidates often omit explaining why LLMs hallucinate, which involves the model generating statistically plausible text without truth verification. The article provides a concise technical breakdown without code, contrasting senior-level responses from junior ones.