AI Engineer Interview Prep Guide

Cover the pipeline: document ingestion with chunking strategy (semantic chunking vs fixed-size with overlap), embedding model selection (considerations for domain-specific vs general embeddings), vector database with metadata filtering, retrieval with hybrid search (dense + sparse), reranking with a cross-encoder, and generation with source citations. Address hallucination mitigation, chunk size optimization, and how to evaluate retrieval quality with precision@k and recall@k metrics.

Use multi-level evaluation: automated metrics (BLEU, ROUGE, BERTScore for similarity), LLM-as-judge for quality assessment, human evaluation for ground truth, and A/B testing for user impact. Implement a golden dataset with labeled examples, regression testing on prompt changes, and continuous monitoring of output quality in production. Discuss the challenges of evaluating open-ended generation and how to handle evaluation drift.

Design agent roles with clear responsibilities: researcher agent (web search, document retrieval), analyst agent (data processing, pattern identification), and writer agent (report generation). Use a orchestrator pattern for task decomposition and result synthesis. Discuss inter-agent communication protocol, shared memory/context, error handling when an agent fails, and how to prevent infinite loops. Address token budget management across agents.

Investigate at each pipeline stage: is the retrieval returning relevant documents (retrieval quality issue), is the context being properly passed to the model (engineering issue), or is the model generating beyond its context (hallucination tendency)? Implement grounding verification: check generated claims against retrieved sources. Add guardrails: constrain output to retrieved information only, add confidence scores, and implement fallback responses when retrieval confidence is low.

RAG: best for large document corpora with frequent updates. Function calling: best for structured data access, calculations, and API interactions. Fine-tuning: best for teaching the model new behaviors, formats, or domain knowledge that is stable. Context caching: best for frequently accessed static context that is expensive to retrieve. Discuss hybrid approaches and the cost-latency-quality tradeoffs of each. Address when to combine multiple approaches for a single application.

Build a prompt registry with version control (not in source code, but in a dedicated system). Implement A/B testing with traffic splitting and metric comparison. Add rollback capability with instant prompt swaps. Track analytics: latency, token usage, quality scores, and user feedback per prompt version. Discuss prompt templates with variable injection, guardrails for prompt injection prevention, and how to manage prompts across development, staging, and production environments.

Discuss specific challenges: latency optimization (caching, streaming, model selection), cost management (token optimization, model routing), quality assurance (evaluation framework, edge case handling), reliability (fallback strategies, retry logic), and monitoring (output quality tracking, drift detection). Include concrete metrics: latency p99, cost per query, accuracy improvements, and user satisfaction scores.

Implement defense in depth: input validation (detect prompt injection patterns, classify user intent), output filtering (content moderation API, PII detection, topic restriction), system prompt protection (instruction hierarchy, delimiter isolation), and monitoring (log all inputs/outputs for audit, anomaly detection on usage patterns). Discuss the tradeoff between safety and usability, and how to handle edge cases where guardrails are too aggressive.