Generative AI

Compare a hosted model API with a self-hosted or open-weight model.

Compares managed model APIs with operating open-weight models across control, expertise, privacy, scale, latency, cost, and portability.

Compare inference, training, fine-tuning, prompting, and RAG.

Separates runtime model use from model training, fine-tuning, prompt design, and retrieval-augmented generation as different engineering levers.

How do you balance quality, latency, and cost in an AI feature?

Balances AI quality, tail latency, and cost through explicit product thresholds, model choice, context control, caching, routing, and measurement.

How would you choose a model for a product feature?

Chooses a model from task-specific evaluation, operating constraints, safety, latency, cost, context, modality, and provider requirements.

How would you design a safe first LLM feature for an existing product?

Designs a bounded and reversible first AI feature with measurable value, controlled data, validation, evaluation, observability, fallback, and human oversight.

What does hallucination mean in an AI application?

Defines hallucination as unsupported or incorrect model output and connects it to evidence, task design, evaluation, and user-visible uncertainty.

AI Engineering

Applied AI interview preparation grounded in real product and production decisions.

An applied AI engineering path covering model behavior, tokens, context, output controls, embeddings, model selection, reliability boundaries, and production diagnosis.