{
  "version": "https://jsonfeed.org/version/1.1",
  "title": "latent — AI systems, logged",
  "home_page_url": "https://ai-blog.oriz.in/",
  "feed_url": "https://ai-blog.oriz.in/feed.json",
  "description": "A working log on building AI systems that survive production — RAG, tool-using agents, and honest evals. By Chirag Singhal.",
  "authors": [
    {
      "name": "Chirag Singhal",
      "url": "https://oriz.in/about/"
    }
  ],
  "language": "en",
  "items": [
    {
      "id": "https://ai-blog.oriz.in/blog/evaluating-llm-outputs/",
      "url": "https://ai-blog.oriz.in/blog/evaluating-llm-outputs/",
      "title": "Evaluating LLM outputs without fooling yourself",
      "content_text": "Vibes are not a metric. A practical framework for measuring whether your LLM feature is good — grounded in test sets, rubrics, and the honest limits of LLM-as-judge.",
      "summary": "Vibes are not a metric. A practical framework for measuring whether your LLM feature is good — grounded in test sets, rubrics, and the honest limits of LLM-as-judge.",
      "date_published": "2026-08-11T00:00:00.000Z",
      "date_modified": "2026-08-11T00:00:00.000Z",
      "authors": [
        {
          "name": "Chirag Singhal",
          "url": "https://oriz.in/about/"
        }
      ],
      "tags": [
        "Evals",
        "LLM-as-Judge",
        "Metrics",
        "Testing",
        "Benchmarks"
      ]
    },
    {
      "id": "https://ai-blog.oriz.in/blog/mcp-real-tools-for-llms/",
      "url": "https://ai-blog.oriz.in/blog/mcp-real-tools-for-llms/",
      "title": "MCP: giving language models real tools",
      "content_text": "The Model Context Protocol turns an LLM from a text generator into something that can read your files, call your APIs, and act. Here is what it is and how to build a server that does not misbehave.",
      "summary": "The Model Context Protocol turns an LLM from a text generator into something that can read your files, call your APIs, and act. Here is what it is and how to build a server that does not misbehave.",
      "date_published": "2026-08-08T00:00:00.000Z",
      "date_modified": "2026-08-08T00:00:00.000Z",
      "authors": [
        {
          "name": "Chirag Singhal",
          "url": "https://oriz.in/about/"
        }
      ],
      "tags": [
        "MCP",
        "Agents",
        "Tools",
        "LLM",
        "Protocol"
      ]
    },
    {
      "id": "https://ai-blog.oriz.in/blog/rag-pipelines-that-work/",
      "url": "https://ai-blog.oriz.in/blog/rag-pipelines-that-work/",
      "title": "RAG pipelines that actually work in production",
      "content_text": "Most RAG demos fall apart on real corpora. Here is the retrieval stack that survives messy documents, ambiguous queries, and users who paste in nonsense.",
      "summary": "Most RAG demos fall apart on real corpora. Here is the retrieval stack that survives messy documents, ambiguous queries, and users who paste in nonsense.",
      "date_published": "2026-08-05T00:00:00.000Z",
      "date_modified": "2026-08-11T00:00:00.000Z",
      "authors": [
        {
          "name": "Chirag Singhal",
          "url": "https://oriz.in/about/"
        }
      ],
      "tags": [
        "RAG",
        "Retrieval",
        "Embeddings",
        "Vector Search",
        "Reranking"
      ]
    }
  ]
}