{
  "id": "research-report-agent",
  "title": "Research Report Agent",
  "level": 2,
  "levelName": "Builder",
  "tagline": "Turn a question and a pile of documents into a report where every sentence can be checked.",
  "summary": "Build the pipeline behind deep research tools: search a corpus, pull exact snippets, plan the research, write only from evidence, verify every claim, publish an HTML report with footnotes and a trace, then score it on checked-in public evaluation fixtures.",
  "usefulFor": [
    "Prepare a technical decision memo whose claims link to the supporting source passages.",
    "Compare reports after source documents change and inspect which claims need revision."
  ],
  "youWillBuild": "An evidence ledger for technical decisions showing how source updates change published claims.",
  "hours": 20,
  "languages": [
    "Rust",
    "Python",
    "TypeScript"
  ],
  "status": "ready",
  "source": "core",
  "author": {
    "name": "AI Engineering from Scratch",
    "github": "rohitg00"
  },
  "skills": [
    "BM25 retrieval",
    "snippet extraction with offsets",
    "research planning",
    "record and replay model calls",
    "citation grounding",
    "claim verification",
    "run budgets and terminal states",
    "tracing",
    "public fixture evaluation"
  ],
  "prerequisites": [
    {
      "title": "Retrieval augmented generation",
      "path": "phases/11-llm-engineering/06-rag"
    },
    {
      "title": "Structured outputs",
      "path": "phases/11-llm-engineering/03-structured-outputs"
    },
    {
      "title": "The agent loop",
      "path": "phases/14-agent-engineering/01-the-agent-loop"
    },
    {
      "title": "Verification gates",
      "path": "phases/14-agent-engineering/38-verification-gates"
    }
  ],
  "stages": [
    {
      "id": "01-search-the-corpus",
      "title": "Search the corpus",
      "summary": "Load markdown documents with headers and rank them with BM25.",
      "hours": 5,
      "concepts": [
        "document parsing",
        "tokenization",
        "BM25",
        "IDF"
      ],
      "difficulty": "core",
      "language": "rust+python",
      "languageWhy": "Rust owns ranking and JSON parsing; Python exposes a small process adapter to later stages.",
      "figure": "pj-rra-bm25",
      "runners": [
        {
          "language": "rust"
        },
        {
          "language": "python"
        }
      ]
    },
    {
      "id": "02-extract-snippets",
      "title": "Extract snippets you can cite",
      "summary": "Split documents into sentences and keep exact character offsets for every snippet.",
      "hours": 2,
      "concepts": [
        "sentence spans",
        "offsets",
        "snippet scoring",
        "context-free sentences"
      ],
      "difficulty": "starter",
      "language": "python",
      "languageWhy": "Python string slices preserve the exact evidence spans used by the verifier.",
      "figure": "pj-rra-snippet-offsets",
      "runners": [
        {
          "language": "python"
        }
      ]
    },
    {
      "id": "03-plan-the-research",
      "title": "Plan the research",
      "summary": "Split the question into facets with a rule planner, then swap in a recorded model safely.",
      "hours": 2,
      "concepts": [
        "query decomposition",
        "Model protocol",
        "record and replay",
        "fallbacks"
      ],
      "difficulty": "core",
      "language": "python",
      "languageWhy": "A Python protocol isolates planning from live model transport and recorded replies.",
      "figure": "pj-rra-plan-facets",
      "runners": [
        {
          "language": "python"
        }
      ]
    },
    {
      "id": "04-write-with-citations",
      "title": "Write only from evidence",
      "summary": "Compose sections from snippets and reject any sentence without a valid citation.",
      "hours": 2,
      "concepts": [
        "extractive writing",
        "citation format",
        "validators"
      ],
      "difficulty": "core",
      "language": "python",
      "languageWhy": "Python dataclasses keep snippets, sections and citation references explicit.",
      "figure": "pj-rra-cited-writer",
      "runners": [
        {
          "language": "python"
        }
      ]
    },
    {
      "id": "05-verify-every-claim",
      "title": "Verify every claim",
      "summary": "Catch swapped names, flipped negations and changed numbers, under a run budget.",
      "hours": 2,
      "concepts": [
        "claim support",
        "poisoned drafts",
        "budgets",
        "terminal states"
      ],
      "difficulty": "core",
      "language": "python",
      "languageWhy": "Python sets and counters make support rules and budgets inspectable.",
      "figure": "pj-rra-critic",
      "runners": [
        {
          "language": "python"
        }
      ]
    },
    {
      "id": "06-publish-the-report",
      "title": "Publish the report",
      "summary": "Render HTML with numbered footnotes and write a trace of the whole run.",
      "hours": 2,
      "concepts": [
        "HTML rendering",
        "footnotes",
        "tracing",
        "pipelines"
      ],
      "difficulty": "core",
      "language": "python+typescript",
      "languageWhy": "Python serializes evidence; TypeScript validates it and renders accessible HTML.",
      "figure": "pj-rra-publish",
      "runners": [
        {
          "language": "python"
        },
        {
          "language": "typescript"
        }
      ]
    },
    {
      "id": "07-score-the-report",
      "title": "Score the public fixtures",
      "summary": "Measure citation precision, source recall and fact coverage on checked-in public evaluation fixtures.",
      "hours": 2,
      "concepts": [
        "public fixture evaluation",
        "citation precision",
        "recall",
        "scorecards"
      ],
      "difficulty": "stretch",
      "language": "python",
      "languageWhy": "Python computes per-question metrics and a reproducible aggregate score.",
      "figure": "pj-rra-scorecard",
      "runners": [
        {
          "language": "python"
        }
      ]
    }
  ],
  "figures": true,
  "overviewFigure": "pj-rra-pipeline",
  "languageReasons": [
    {
      "language": "Rust",
      "why": "Build the BM25 index and parse JSON requests using only the standard library."
    },
    {
      "language": "Python",
      "why": "Compose evidence extraction, recorded model calls, verification and evaluation."
    },
    {
      "language": "TypeScript",
      "why": "Validate the evidence contract and render an interactive, portable HTML report."
    }
  ],
  "demo": {
    "command": [
      "python3",
      "run_report.py",
      "How does a secrets proxy protect API keys from prompt injection?",
      "--out",
      "out"
    ],
    "cwd": "solution"
  },
  "demos": [
    {
      "gif": "media/run.gif",
      "poster": "media/run.png",
      "title": "Initialize and grade",
      "caption": "Recorded starter failure followed by all reference-solution stages passing. Reference passes do not earn learner certificates."
    },
    {
      "gif": "media/output.gif",
      "poster": "media/output.png",
      "title": "Inspect the output",
      "caption": "Real browser recording of the generated HTML report, source hover evidence, and expanded run trace."
    }
  ]
}