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AI Production Scenarios & Labs - AI Engineering | Aporeon

AI Production Scenarios & Labs

Focused implementation, debugging, evaluation, incident response, and architecture-review exercises.

12 drillsPractice one at a timeOrdered pathCoding problem

Recommended start

Validated AI extraction

Builds a schema-constrained extraction boundary that distinguishes refusal, malformed output, semantic invalidity, and safe bounded repair.

Medium~30 minStep 1 of 12

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1Validated AI extractionRecommended startBuilds a schema-constrained extraction boundary that distinguishes refusal, malformed output, semantic invalidity, and safe bounded repair.Coding problemMedium~30 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

Practice order

12 drillsOne at a time

1Validated AI extractionUp next / Medium / ~30 min
2Verified hybrid RAGHard / ~40 min
3Diagnose RAG regressionHard / ~35 min

2

Verified hybrid RAGBuilds a hybrid retrieval pipeline that enforces tenant scope before ranking, assembles a bounded context, and verifies returned citations.Coding problemHard~40 minArtificial IntelligenceGenerative AILarge Language Models

Practice

3Diagnose RAG regressionUses versioned traces and evaluation slices to locate whether a RAG regression came from ingestion, retrieval, context assembly, or generation.Coding problemHard~35 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

4Reliable model gatewayBuilds a provider-neutral model gateway that owns request deadlines, retry classification, budget enforcement, fallback policy, and versioned telemetry.Coding problemHard~40 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

5AI evaluation release gateBuilds a reproducible evaluation harness that records immutable cases, versioned outputs, deterministic checks, calibrated graders, slices, and release decisions.Coding problemHard~40 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

6Safe AI request tracingInstruments an AI workflow with correlated stage spans, low-cardinality versions, usage and outcome signals, and explicit content-capture controls.Coding problemMedium~30 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

7Bounded approved tool workflowBuilds a resumable tool workflow that validates model proposals, authorizes every action, requires approval for side effects, and remains idempotent under retries.Coding problemHard~40 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

8Diagnose agent loopUses workflow traces to distinguish planning failure, stale observations, retry ambiguity, and missing terminal conditions in a looping agent.Coding problemHard~35 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

9Prompt-injection incidentRuns containment and recovery for an agent that ingested malicious instructions from retrieved content and attempted an unauthorized outbound tool call.Coding problemHard~40 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

10Poisoned RAG recoveryHandles a poisoned retrieval corpus by freezing ingestion, tracing provenance, switching immutable index versions, rebuilding clean data, and proving recovery.Coding problemHard~40 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

11Review managed AI architectureReviews a managed AI proposal against data flow, identity, region, retention, quota, observability, portability, and recovery requirements.Coding problemHard~35 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

12AI production game dayCombines quality, reliability, cost, security, privacy, and recovery evidence into a realistic launch and incident game day.Coding problemHard~45 minArtificial IntelligenceGenerative AILarge Language ModelsPractice

4Reliable model gateway

Hard / ~40 min

5AI evaluation release gateHard / ~40 min

6Safe AI request tracingMedium / ~30 min

7Bounded approved tool workflowHard / ~40 min

8Diagnose agent loopHard / ~35 min

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