Fine-Tuning Interview Questions PDF

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

Model question on RLHF — SFT, reward model, PPO alignment, and how human preferences change helpfulness vs hallucination trade-offs.

RAG has become the foundational architecture for production GenAI applications at companies like Notion, Duolingo, and Morgan Stanley. Interviewers expect you to explain the full retrieval pipeline — not just define the acronym. Follow along to master what RAG is, when to use it over fine-tuning, and how to articulate trade-offs that separate junior from senior candidates.

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.

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

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

**3.1** 128k context, stronger instruction following — update inference stack for long context KV cache.

**Use cases** Where zero-shot vision fails consistently on proprietary visual patterns.

**Decision** Try prompt + tools + RAG first; fine-tune if style/reliability gap persists and data exists.

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

Company/scenario question on open vs closed LLMs — self-hosting economics, compliance, upgrade velocity, and hybrid strategies.

Hard model question comparing CAI, RLHF (PPO), and DPO — data efficiency, stability, and production fine-tuning trade-offs.

Hard scenario on LLM cost control — token budgeting, routing, caching, batch APIs, and unit economics at millions of queries.

Scenario question comparing prompting, RAG, and fine-tuning for domain knowledge — freshness, cost, auditability, and when to combine approaches.