ai-engineering-from-scratch/phases/14-agent-engineering/24-agent-observability-platforms/quiz.json at main · Amjad-AbuRmileh/ai-engineering-from-scratch · GitHub

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{
  "lesson": "24-agent-observability-platforms",
  "title": "Agent Observability: Langfuse, Phoenix, Opik",
  "questions": [
    {
      "stage": "pre",
      "question": "What license does Langfuse ship under after the June 2025 open-sourcing of formerly commercial modules?",
      "options": [
        "GPLv3",
        "MIT",
        "Elastic License 2.0",
        "Apache 2.0"
      ],
      "correct": 1,
      "explanation": "Langfuse is MIT, including LLM-as-a-judge, annotation queues, prompt experiments, and Playground after June 2025."
    },
    {
      "stage": "pre",
      "question": "What is Arize Phoenix strongest at according to the lesson?",
      "options": [
        "Prompt versioning",
        "Deep agent-specific evaluation: trace clustering, anomaly detection, RAG retrieval relevancy, OpenInference auto-instrumentation",
        "Automated optimization loop",
        "Static analysis of code"
      ],
      "correct": 1,
      "explanation": "Phoenix focuses on behavioral drift and RAG evaluation with OpenInference auto-instrumentation."
    },
    {
      "stage": "check",
      "question": "Which platform pairs automated prompt optimization with guardrails (PII redaction, topical constraints) and LLM-judge hallucination detection?",
      "options": [
        "Langfuse",
        "Phoenix",
        "Opik",
        "Jaeger"
      ],
      "correct": 2,
      "explanation": "Opik centers on the optimization + guardrail loop."
    },
    {
      "stage": "check",
      "question": "Which platform does the lesson recommend for an all-in-one with prompt management?",
      "options": [
        "Langfuse",
        "Phoenix",
        "Opik",
        "Datadog APM"
      ],
      "correct": 0,
      "explanation": "Langfuse covers tracing + prompt management + evals + session replay end-to-end."
    },
    {
      "stage": "check",
      "question": "What does the lesson say about vendor-published platform benchmarks?",
      "options": [
        "They are definitive",
        "Take them as directional; measure your own corpus",
        "They are forbidden by OpenTelemetry",
        "Only Opik's are reliable"
      ],
      "correct": 1,
      "explanation": "Even the cited 14x gap between Opik and Langfuse should be measured on your own data before deciding."
    },
    {
      "stage": "post",
      "question": "Why is tracing without evaluation considered expensive logging?",
      "options": [
        "Disk costs more than CPU",
        "You see runs but you do not score them, so regressions are invisible and bisection is impossible",
        "Providers charge for spans",
        "Spans cannot be replayed"
      ],
      "correct": 1,
      "explanation": "Evals are what turn traces into actionable quality signals."
    },
    {
      "stage": "post",
      "question": "What is the lesson's warning about self-rolled LLM-judges?",
      "options": [
        "They are forbidden by Apache 2.0",
        "CRITIC applies: judges need external grounding for factual verification or they rubber-stamp",
        "They only work in TypeScript",
        "They must run on GPUs"
      ],
      "correct": 1,
      "explanation": "Without external tool-grounded verification, LLM-judges drift toward rubber-stamping (CRITIC, Lesson 5)."
    }
  ]
}