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Topic
Gateways, orchestration, deterministic boundaries, state, failures, fallbacks, and versioning.
Practice items tagged with AI Application Architecture.
Diagnoses an AI regression by decomposing request stages, configuration changes, token growth, retries, routing, tools, retrieval, caching, and provider behavior.
Compares managed model APIs with operating open-weight models across control, expertise, privacy, scale, latency, cost, and portability.
Separates model and provider outcomes, applies bounded recovery, validates outputs, and exposes useful status and telemetry.
Chooses a model from task-specific evaluation, operating constraints, safety, latency, cost, context, modality, and provider requirements.
Designs a bounded and reversible first AI feature with measurable value, controlled data, validation, evaluation, observability, fallback, and human oversight.
Treats model changes as controlled releases using versioned configuration, fixed evaluations, shadow or canary traffic, monitoring, and rollback.