Anthropic Prompt Engineer Interview Questions

Prompt engineering focuses on instruction wording. **Context engineering** focuses on the full state fed to the model each step: policies, repository maps, retrieved files, prior tool outputs, memories, and task specs.

Medium multimodal prompting question for UI bug diagnosis.

Medium prompt engineering question on prompt unit tests and CI gates.

Prompt patterns for prohibitions, scoped refusals, alternatives, and measuring over-refusal vs under-refusal.

Hard prompt design on instruction stacking, jailbreak override attempts, and provider-specific precedence rules.

Hard context engineering trade-offs: prioritization tiers, dynamic reallocation, and what to drop first under pressure.

Company-based prompt design applying principle hierarchies, helpful-harmless-honest trade-offs, and critique passes inspired by Constitutional AI.

Compress agent memory into durable distilled state while preserving goals, constraints, and tool outcomes.

Multi-agent prompt patterns where distinct personas argue, review, or refine outputs before final synthesis.

Protect instructions, secrets, policy, and tool policies in system prompts against leakage, override, and indirect injection.

Build golden sets, error taxonomy, automated graders, and human review loops to iterate prompts like production software.

Context budget tactics: summarization, selective retrieval, example pruning, and distillation for long-running agent sessions.

Evaluate persona and role prompts for tone, domain framing, and when they add noise without improving task accuracy.

Structured prompting patterns using tags and boundaries to separate instructions, context, tools, and user input in production LLM apps.

Security-focused prompt engineering on jailbreaks, indirect injection, tool sandboxing, and layered defenses for production chatbots and agents.

Foundational prompt engineering question on instruction placement, example selection, and when demonstrations beat bare instructions.

MCP multiplies agent power and attack surface.

Prompt injection is the #1 security interview topic for agents that read untrusted text.

Advanced scenario question on why LLMs hallucinate — next-token prediction vs fact verification, root causes, and production defenses asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Part 2 of the LLM hallucination handbook — factual, citation, reasoning, math, code, temporal, context, and tool-use hallucinations; decoding risks; RAG failure modes; production case study.

Part 3 of the hallucination handbook — production defense layers from retrieval and prompting through guardrails, citations, confidence scoring, eval frameworks, and enterprise architecture. Asked at OpenAI, Google, Meta, Anthropic, Microsoft, and Amazon.

Medium Claude cost question on prompt caching breakpoints and hit rates.

**Architecture** LangGraph with interrupt before sensitive tools (lg-004); Bedrock or enterprise API.

**Diagnosis** Categorize refusals: policy vs ambiguous vs bug; replay with rubric.