Meta AI Prompt Engineer Interview Questions

Cross-language prompt design covering translation drift, code-switching, locale formatting, and eval gaps in multilingual products.

Company-wide context is a platform product.

Centralized prompt storage with versioning, approvals, environment promotion, audit trails, and runtime serving.

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.

Hard Llama/inference question on continuous batching and GPU efficiency.

**Usage** Separate small model for moderation latency.

Project question on synthetic data — distillation datasets, diversity, contamination, quality filters, and human review.

Hard scenario on jailbreaks — DAN, prompt injection, alignment limits, red-teaming, and layered defenses.

Hard project question on INT8/INT4 quantization, knowledge distillation, and serving smaller models for cost and latency.

Hallucination in RAG systems is the #1 production failure mode cited in AI engineering interviews. Your interviewer wants a systematic debugging framework — not a list of buzzwords. Learn how to measure faithfulness, fix retrieval precision, and layer mitigations the way senior engineers at Databricks and Meta actually ship RAG.

Self-hosting Llama 3 is a infrastructure-heavy question for ML platform and AI engineer roles at Meta-adjacent companies. Expect deep dives on quantization, vLLM, GPU sizing, and the TCO math that determines build vs buy decisions.

Medium RAG interview on hard negatives for embedding/reranker quality.

Model-based interview on DPO vs RLHF — preference pairs, training simplicity, and production trade-offs.

LLM interview topic on distillation — teacher/student training, latency/cost wins, and quality regression risks.

**Dimensions** Quality, cost curve, latency, compliance, ops burden, roadmap velocity.

**Analysis** Rank features by volume, quality bar, data sensitivity benefiting on-prem.

**Key clauses** 700M MAU threshold triggers extra terms; AUP prohibits harmful uses.

**Metrics** Requests queued, p95 latency, GPU memory headroom.

**Constraints** No phone-home telemetry; offline license compliance documentation.

**Training** Refusal datasets, toxicity filters on training data.

**Training** Rank selection, learning rate, epochs; validation loss + task evals.

**GPTQ/AWQ** GPU inference friendly; integrate with vLLM/TGI depending on support.