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Pick a focused question that fits your time, stack, and interview goal.
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Stores only useful, scoped, attributable memories with extraction, verification, retrieval, expiry, correction, and deletion.
Coordinates lifecycle rules across application state, provider storage, files, embeddings, caches, traces, evaluations, and backups.
Treats streams as event protocols with partial-output UX, cancellation, validation, moderation, and terminal outcome semantics.
Makes AI involvement, limitations, evidence, consequential decisions, correction, escalation, and accountability visible to users.
Separates traffic, data, dependency, model, prompt, and grader change while maintaining stable anchors and fresh evaluation.
Creates a task-oriented model boundary that normalizes application outcomes without erasing useful provider capabilities.
Allocates per-stage deadlines and spend, controls tokens and fan-out, and optimizes for successful outcomes rather than cheap calls.
Carries source permissions into ingestion and query-time filtering while failing closed across retrieval, caching, and citations.
Evaluates verified outcomes, trajectory quality, safety, efficiency, recovery, and important slices in realistic environments.
Judges claims against allowed evidence, separates correctness from support, and tests whether the system answers or abstains appropriately.
Builds a query-and-relevance dataset and separates retrieval recall, ranking quality, context quality, and generated-answer quality.
Measures schema validity, tool selection, argument correctness, policy, execution outcome, repair, and final user result.
Connects answer claims to retrieved evidence while preventing decorative citations and preserving source-level traceability.
Controls admission, concurrency, queues, tenant fairness, token budgets, and overload behavior before provider throttling cascades.
Coordinates source changes, versions, tombstones, reconciliation, and query-time freshness without treating the index as authoritative.
Keeps credentials in trusted executors and replaces broad reusable secrets with scoped references and short-lived authority.
Uses stable operation identities, persisted states, deduplication, side-effect keys, and replay rules across requests and workers.
Separates durable application state from the model's bounded request context while preserving ownership, concurrency, and deletion.
Rebuilds and compares incompatible vector spaces using versioned indexes, shadow queries, atomic cutover, and rollback.
Applies purpose limitation, selective retrieval, redaction, pseudonymization, and field-level policy before model calls.
Constrains source-to-sink combinations with least privilege, egress policy, argument checks, approval, and information-flow awareness.
Carries trusted tenant scope through retrieval, caches, memory, tools, traces, evaluations, and asynchronous execution.
Checkpoints authoritative state, reconciles ambiguous tool outcomes, preserves approvals, and resumes without repeating completed work.
Builds system-specific adversarial testing around realistic assets, identities, channels, tools, and measurable impact.