Question 1

What is retrieval-augmented generation (RAG), and what problems does it solve compared to fine-tuning?

Accepted Answer

RAG: inject relevant context from a knowledge base at inference time. Solves knowledge staleness and hallucination on domain-specific queries without retraining. Fine-tuning changes model weights — better for style/behavior, not for factual grounding.

Question 2

Explain the difference between temperature, top-p, and top-k sampling in language model inference.

Accepted Answer

Temperature scales the logit distribution (higher = more random). Top-k limits the vocabulary to the k most likely tokens. Top-p (nucleus) keeps tokens summing to probability p. They're often combined: top-p + temperature.

Question 3

What causes hallucination in LLMs, and what engineering strategies reduce it in production systems?

Accepted Answer

Hallucination: model generates plausible but false text when confident in uncertain territory. Mitigations: RAG with source citations, constrained output formats, self-consistency sampling, and post-hoc fact-checking with a verification model.

Question 4

How do you evaluate an LLM-powered feature in production when ground truth labels are expensive or unavailable?

Accepted Answer

LLM-as-judge evaluations, human preference labeling (small sample), behavioral A/B tests on downstream metrics, and automatic evaluation with reference-free metrics like BERTScore or custom rubrics.

Question 5

Tell me about a production AI feature you shipped. What was the hardest engineering problem you solved?

Accepted Answer

Focus on reliability engineering: latency management, prompt versioning, output validation, fallback logic, and how you monitored quality after launch.

Question 6

Describe a time an AI feature you built produced unexpected or harmful outputs in production. How did you respond?

Accepted Answer

Show rapid response: detection mechanism, immediate mitigation (rate limit, disable feature), root cause analysis, and systematic safeguards added to prevent recurrence.

Question 7

Tell me about a time you had to explain the limitations of an AI system to a stakeholder who expected it to be fully reliable.

Accepted Answer

Cover probabilistic outputs, failure modes (hallucination, adversarial inputs), and how you framed human-in-the-loop requirements. Show you're an AI realist, not an AI hype agent.

Question 8

Design a production RAG system for a company's internal knowledge base with 500,000 documents.

Accepted Answer

Cover chunking strategy (size, overlap, by section), embedding model selection, vector database (pgvector, Pinecone, Weaviate), hybrid search (BM25 + vector), reranking, context assembly, and cache layer for common queries.

Question 9

How would you design an AI-powered customer support system that handles 10,000 tickets per day?

Accepted Answer

Cover intent classification, RAG for knowledge retrieval, escalation routing to humans for complex/sensitive issues, human review queue, feedback loop for answer quality, and latency/cost budget per ticket.

Question 10

Design a prompt management system for a team of 20 engineers building multiple AI features.

Accepted Answer

Cover prompt versioning (git-tracked or DB-backed), A/B testing framework for prompt variants, evaluation harness per prompt, rollback capability, and cost/quality tracking per version.

AI Engineer Interview Questions 2026

Technical

Behavioral

System Design