Ai Engineering Interviewv1.0.1

A candidate is presented with a scenario where a RAG-powered customer support bot is returning incorrect answers. They must diagnose potential failure points in retrieval, generation, or evaluation and propose fixes.

Design an autonomous agent that can handle a complex, multi-step task like trip planning or code debugging. Discuss tool selection, orchestration, error recovery, and safety guardrails.

Given a domain-specific LLM application, decide whether to fine-tune with LoRA, use prompt engineering, or adopt RAG. Justify based on cost, latency, data availability, and performance needs.

Design an evaluation framework for a new LLM feature from offline evals to online monitoring. Identify key metrics, datasets, and methods to detect regressions and drift.

Architect the serving infrastructure for a multi-tenant LLM platform with strict latency and cost constraints. Consider GPU choices, scaling policies, observability, and cost optimization.

Offer LLM capabilities via API, charging per token or request. This provides predictable revenue based on usage and appeals to developers and enterprises.

Combine a base subscription with premium tiers for higher usage limits or advanced features. For AI interview prep, this could offer a free tier with limited daily questions and paid tiers with full question banks, feedback, and analytics.

License the skill or platform to organizations for internal technical training and interview preparation. This provides high-value contracts and bulk access.