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Protects knowledge ingestion with source trust, provenance, validation, quarantine, review, versioning, and rollback.
Builds a readable request with stable instructions and clearly delimited, provenance-aware untrusted inputs.
Classifies tools by consequence and applies distinct permissions, validation, confirmation, idempotency, and audit controls.
Combines deterministic invariants, severity-aware quality thresholds, slice protection, baseline comparison, and guarded rollout.
Separates user-visible progress from validated completion and prevents partial JSON or tool arguments from triggering side effects.
Maps assets, trust boundaries, untrusted content, model influence, actions, and abuse cases before selecting controls.
Maps risk tiers and policy statements to owned, versioned, testable runtime controls, evidence, exceptions, and review triggers.
Uses inference profiles for capacity while accounting for destination Regions, IAM and SCP policy, residency, quotas, and observable routing.
Selects a production experiment based on user exposure, side effects, measurement needs, cost, and rollback risk.
Checks model, feature, API, region, quota, commercial access, lifecycle, and dependent-service compatibility before rollout.
Builds a resumable tool workflow that validates model proposals, authorizes every action, requires approval for side effects, and remains idempotent under retries.
Builds a provider-neutral model gateway that owns request deadlines, retry classification, budget enforcement, fallback policy, and versioned telemetry.
Instruments an AI workflow with correlated stage spans, low-cardinality versions, usage and outcome signals, and explicit content-capture controls.
Runs containment and recovery for an agent that ingested malicious instructions from retrieved content and attempted an unauthorized outbound tool call.
Reviews a managed AI proposal against data flow, identity, region, retention, quota, observability, portability, and recovery requirements.
Combines quality, reliability, cost, security, privacy, and recovery evidence into a realistic launch and incident game day.
Diagnoses schema-valid quality regressions across inputs, context, prompts, examples, models, schemas, tools, and downstream interpretation.
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.
Defines hallucination as unsupported or incorrect model output and connects it to evidence, task design, evaluation, and user-visible uncertainty.
Explains RAG as a runtime retrieval boundary that supplies relevant, current, and governed evidence without changing model weights.
Defines a large language model through next-token prediction and connects that mechanism to useful language behavior and its limits.
Defines AI evaluation as repeatable measurement of application behavior while preserving deterministic tests for code and contracts.