LLM Agents Interview Questions, issue 3, Feb 24, 2026
The Static Few-Shot Trap
Senior AI Engineer interview at Anthropic, and the interviewer asks:
“You’re deploying an LLM to solve highly niche competitive programming problems. Static few-shot examples don’t scale. Zero-shot ‘think step-by-step’ fails without domain context. You cannot use an external vector DB for RAG. How do you force the model to dynamically generate its own relevant context?”
Don’t say: “I’ll just cram the system prompt with 50 diverse examples and leverage a 1M token context window.”
Hard-coding examples scales token count and latency, but it doesn’t guarantee relevance - forcing the model to self-generate analogs aligns attention with the exact solution manifold.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.