Fine-Tuning Interview Questions for 3 Years Experience

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

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.

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

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

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.

LangGraph has emerged as the go-to framework for stateful agent workflows, replacing brittle LangChain chains in production. Interviewers want graph thinking — nodes, edges, conditional routing, and checkpointing — not just API familiarity. Essential for any LangChain shop hiring GenAI engineers.

Structured JSON output from LLMs is a production necessity at Stripe, Vercel, and every AI-native startup. Interviewers test your reliability engineering — schema enforcement, retry logic, streaming edge cases — not just 'use JSON mode.' Master the full production playbook.

OpenAI's model lineup changes fast. GPT-4 vs GPT-4o is a model selection question that tests whether you understand latency, cost, multimodal capabilities, and when reasoning depth matters. Critical for any role touching OpenAI APIs in production.

Constitutional AI is Anthropic's differentiator and a must-know for Claude-focused interviews. Go beyond the marketing — explain the self-critique training loop, how CAI compares to RLHF, and practical safety implications for production deployments.

Google's Gemini 1.5 Pro long-context window opens use cases impossible with standard LLMs — whole-codebase analysis, multi-hour video, massive document review. Interviewers test whether you understand real limitations behind the 1M token marketing number.

Claude Code is Anthropic's CLI coding agent designed for repository-level work: reading the codebase, editing multiple files, running tests/builds, and iterating until a task succeeds. Unlike autocomplete-first copilots that live inside the editor, Claude Code operates as an agent loop — observe → plan → act (edit/run tools) → verify.

Interviewers want a practical comparison, not marketing.

Codex CLI represents OpenAI's push into agentic coding outside the editor — similar category to Claude Code and other CLI agents. The core loop is: understand task → explore repo → edit → run commands → iterate.

A production AI coding agent typically includes:

MCP (Model Context Protocol) defines a clean separation:

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.

AGENTS.md is becoming the de facto 'README for agents.'

Product teams shouldn't each reinvent agent security.

AI observability extends classic APM.

Scenario interview on defining and communicating hallucination metrics to executives.