AI Application Engineering

An AI feature became slower and more expensive. How would you diagnose it?

Diagnoses an AI regression by decomposing request stages, configuration changes, token growth, retries, routing, tools, retrieval, caching, and provider behavior.

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 temperature and sampling settings affect model output?

Explains how decoding settings reshape token selection and why they must be tuned against task-specific evaluation rather than folklore.

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 should an application handle context overflow and truncated output?

Handles token-budget overflow and incomplete generation through explicit prioritization, rejection, retrieval, summarization, continuation, and validation.

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.