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  "description": "MAANG-level AI Engineer interview prep mapped to the AI Engineering Roadmap 2026 newsletter. Deep conceptual, coding, system design & behavioral questions with detailed answers. Covers LLMs, Python for AI, tool calling, agents, RAG, embeddings, vector DBs, eval frameworks & more. Biweekly added with each episode.",
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    {
      "repo": "promptfoo/promptfoo",
      "reason": "Related to JoshithReddyAleti/AI_Engineer_Interview_Prep through llm eval category and docker deployment."
    },
    {
      "repo": "modelscope/evalscope",
      "reason": "Related to JoshithReddyAleti/AI_Engineer_Interview_Prep through llm eval category and docker deployment."
    },
    {
      "repo": "vibrantlabsai/ragas",
      "reason": "Related to JoshithReddyAleti/AI_Engineer_Interview_Prep through llm eval category and docker deployment."
    },
    {
      "repo": "comet-ml/opik",
      "reason": "Related to JoshithReddyAleti/AI_Engineer_Interview_Prep through llm eval category and docker deployment."
    },
    {
      "repo": "EricLBuehler/mistral.rs",
      "reason": "Related to JoshithReddyAleti/AI_Engineer_Interview_Prep through llm eval category and docker deployment."
    },
    {
      "repo": "Giskard-AI/giskard-oss",
      "reason": "Related to JoshithReddyAleti/AI_Engineer_Interview_Prep through llm eval category and local deployment."
    },
    {
      "repo": "samugit83/redamon",
      "reason": "Related to JoshithReddyAleti/AI_Engineer_Interview_Prep through llm eval category and docker deployment."
    },
    {
      "repo": "UKGovernmentBEIS/inspect_ai",
      "reason": "Related to JoshithReddyAleti/AI_Engineer_Interview_Prep through llm eval category and docker deployment."
    }
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    "benchmark prompts and agents",
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      "evidence": [
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        "Runtime blocker: python.",
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  "summary": {
    "tl_dr": "Use JoshithReddyAleti/AI_Engineer_Interview_Prep when the user needs a llm eval project with docker, local, cloud deployment options.",
    "purpose": "Use JoshithReddyAleti/AI_Engineer_Interview_Prep when the user needs a llm eval project with docker, local, cloud deployment options.",
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      "prompts",
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      "evaluate LLM outputs",
      "benchmark prompts and agents",
      "track model quality",
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      "benchmarking",
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      "edge-only Cloudflare Workers deployment without adaptation",
      "production inference serving",
      "end-user chat apps"
    ],
    "deployment": [
      "docker",
      "local",
      "cloud"
    ],
    "alternatives": []
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        "confidence": "medium",
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          "Matched \"eval\" in metadata."
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      "edge-only Cloudflare Workers deployment without adaptation",
      "Partial or estimated quality signals: stars30dDelta."
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