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Showing the AI Engineering curriculum. Technology tags describe each drill's actual implementation context; shared drills can belong to more than one track.
Uses traces and saturation evidence to locate retry amplification, queue growth, exhausted workers, and failing fallback paths.
Uses traces to separate unclear goals, bad tool interfaces, missing state, contradictory results, poor termination, and retry amplification.
Diagnoses an AI regression by decomposing request stages, configuration changes, token growth, retries, routing, tools, retrieval, caching, and provider behavior.
Reproduces failing cases, compares versioned traces, isolates the first changed component, mitigates impact, and adds regression coverage.
Traces a missed answer through source ingestion, parsing, chunking, filtering, query construction, retrieval, fusion, and ranking.
Builds a reproducible evaluation harness that records immutable cases, versioned outputs, deterministic checks, calibrated graders, slices, and release decisions.
Builds a schema-constrained extraction boundary that distinguishes refusal, malformed output, semantic invalidity, and safe bounded repair.
Builds a hybrid retrieval pipeline that enforces tenant scope before ranking, assembles a bounded context, and verifies returned citations.
Compares managed model APIs with operating open-weight models across control, expertise, privacy, scale, latency, cost, and portability.
Chooses between managed ingestion and retrieval convenience and custom control over indexing, ranking, security, evaluation, and operations.
Chooses delegation patterns based on who owns the user interaction, context, final answer, permissions, and failure handling.
Chooses between a direct provider relationship and a cloud-managed integration using concrete security, operations, capability, and commercial constraints.
Separates the current model input from authoritative workflow state and selectively retained information across runs.
Separates runtime model use from model training, fine-tuning, prompt design, and retrieval-augmented generation as different engineering levers.
Combines exact lexical matching with semantic recall and explains why hybrid retrieval often needs measured fusion and ranking.
Balances burst flexibility, predictable capacity, latency variance, commitment cost, and realistic traffic shape.
Separates prompt-requested JSON, parseable JSON modes, and provider-enforced schema adherence.
Separates application instruction attacks from attempts to bypass a model's safety behavior and explains their overlapping defenses.
Handles a poisoned retrieval corpus by freezing ingestion, tracing provenance, switching immutable index versions, rebuilding clean data, and proving recovery.
Combines restrained autonomy, curated tools, durable state, approval, memory, recovery, MCP trust, evaluation, and operational controls.
Connects product success, layered evals, calibrated graders, release gates, guarded rollout, traces, monitoring, feedback, and incident learning.
Combines governed ingestion, authorized hybrid retrieval, reranking, grounded generation, citations, evaluation, observability, and fallback.
Combines threat modeling, injection containment, data minimization, tenant isolation, tools, audit, incident response, and governance.
Combines explicit trust boundaries, provider access, state, execution modes, resilience, budgets, versioning, and operations into a restrained design.