Easy AI Interview Questions

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.

AI Agents are the hottest topic in 2025–2026 GenAI interviews, but most candidates confuse agents with chatbots. Interviewers at OpenAI and Anthropic want you to articulate the agent loop — perceive, plan, act, reflect — and explain when tool use justifies agent complexity over a simple chain.

The Model Context Protocol is Anthropic's open standard reshaping how LLMs connect to tools and data. If you're interviewing for Claude ecosystem roles or AI platform engineering, expect MCP questions. Understand host vs server architecture and how MCP differs from ad-hoc function calling.

Chain-of-Thought prompting remains one of the most frequently asked prompt engineering questions, yet candidates often give surface-level answers. Learn when CoT helps vs hurts, production token costs, and advanced variants like self-consistency and tree-of-thoughts that impress senior interviewers.

Vector databases power every RAG system, yet most candidates can't explain ANN algorithms or hybrid search. This fundamental question appears in 80% of AI engineering loops. Master dense vs sparse retrieval and when hybrid search wins.

**Projects** Knowledge files in project scope, team collaboration, minimal engineering.

**Nodes** Each node is a Python/JS function (or runnable) that accepts the current graph state and returns a partial update — not necessarily the full state. LangGraph merges updates using reducers defined on the state schema (e.g., append to a list, overwrite a scalar). Nodes should stay focused: one node might call the LLM, another might invoke tools, another might format output.

Agents waste tokens and cause incidents when errors are opaque strings.

MCP exposes three complementary primitives that interviewers use to test whether you understand protocol design, not just buzzwords.

Brute-force exact search thresholds, ANN recall trade-offs, and hybrid exact re-rank on shortlists.

Bootstrap strategies, fallback retrieval, hybrid search defaults, and UX for new tenants or collections.

Embedding geometry and index metric choice — normalization, MIPS, and provider defaults for text retrieval.

Label definitions, calibration examples, abstain classes, and structured outputs for production classifiers without fine-tuning.

Techniques for length limits, bullet formats, tone control, and post-processing to manage verbosity and brand voice.

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.

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

Easy AI Agents interview question on when not to use an agent — architecture, trade-offs, eval, and production patterns.

Easy AI Agents interview question on the ReAct pattern — Thought/Action/Observation loops, trade-offs, and production guardrails.

Easy project question on SSE/token streaming — perceived latency, client backpressure, partial JSON, and disconnect recovery.

Model question on dense vector embeddings — semantic similarity, clustering, classification, dedup, and recommendation beyond vector search RAG.

Decoding parameters interview question — how sampling controls randomness, creativity, and hallucination risk in production.

Foundational model question on BPE/tokenizers — why token counts differ from words, and production impact on cost, limits, and RAG chunking.