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Separates product policy, orchestration, model access, data and tools, validation, state, and operations into explicit application-owned boundaries.
Distinguishes exact, provider, semantic, and intermediate caches while preserving identity, freshness, versioning, and correctness.
Explains the operating, security, governance, and integration capabilities that surround managed model inference without treating a platform as the application.
Explains RAG as a runtime retrieval boundary that supplies relevant, current, and governed evidence without changing model weights.
Defines AI evaluation as repeatable measurement of application behavior while preserving deterministic tests for code and contracts.
Separates what a model can perform from whether the complete AI feature behaves dependably under real inputs, failures, and constraints.
Distinguishes model-directed execution from explicit application control and explains when each is appropriate.
Treats a production prompt as a versioned request contract containing instructions, context, output expectations, and application-owned controls.
Treats model, prompt, schema, tools, routing, retrieval, policy, and evaluators as one observable behavior release.
Connects application, model, retrieval, tools, validation, retries, and outcomes while controlling sensitive telemetry.
Chooses the smallest execution model that satisfies the task while recognizing when explicit multi-step coordination adds real value.
Chooses request-response, streaming, or durable background execution based on duration, user interaction, reliability, and side effects.
Rejects agent complexity when rules, APIs, search, ordinary automation, or a bounded model call solve the task more safely.
Identifies tasks better served by deterministic code, search, rules, or conventional models because of correctness, latency, cost, or control.
Balances one-call simplicity against explicit multi-step workflows with intermediate validation and failure handling.
Evaluates edge execution and gateway features against model capability, data path, limits, observability, caching safety, and platform ownership.
Centralizes cross-cutting model access concerns when multiple applications or providers justify the added hop and operational ownership.
Places validation, authorization, limits, state transitions, and approval outside the model while acknowledging that guardrails reduce rather than eliminate risk.