{
  "version": "2026-08-18.1",
  "claims": [
    {
      "id": "CLAIM-RAG-RUNTIME-RETRIEVAL",
      "statement": "RAG supplies retrieved external information to a generator at inference time instead of encoding every changing fact in model parameters.",
      "status": "VERIFIED",
      "sourceIds": ["SOURCE-RAG-PAPER", "SOURCE-MICROSOFT-RAG-VS-FT"]
    },
    {
      "id": "CLAIM-RAG-FRESHNESS-FIT",
      "statement": "A retrieval path is a suitable candidate when the required knowledge changes independently of the model and the corpus can be maintained.",
      "status": "BOUNDED",
      "sourceIds": ["SOURCE-MICROSOFT-RAG-VS-FT", "SOURCE-PHAROS-GUIDE"]
    },
    {
      "id": "CLAIM-RAG-PROVENANCE",
      "statement": "Retrieved passages can provide provenance for an answer, but the application must still test whether each citation supports the generated claim.",
      "status": "BOUNDED",
      "sourceIds": ["SOURCE-RAG-PAPER", "SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL"]
    },
    {
      "id": "CLAIM-FINE-TUNING-BEHAVIOR",
      "statement": "Fine-tuning is an optimization path for task behavior, format, or specialization; it is not a dependable mechanism for continuously changing facts.",
      "status": "VERIFIED",
      "sourceIds": ["SOURCE-MICROSOFT-RAG-VS-FT", "SOURCE-OPENAI-MODEL-OPTIMIZATION"]
    },
    {
      "id": "CLAIM-EXAMPLES-REQUIRED",
      "statement": "A fine-tuning decision needs representative examples and an evaluation set that reflects the intended task.",
      "status": "VERIFIED",
      "sourceIds": ["SOURCE-OPENAI-MODEL-OPTIMIZATION", "SOURCE-MICROSOFT-RAG-VS-FT"]
    },
    {
      "id": "CLAIM-EVAL-FIRST",
      "statement": "Prompting, retrieval, and training changes should be compared against a defined evaluation baseline rather than selected from an architecture label alone.",
      "status": "BOUNDED",
      "sourceIds": ["SOURCE-OPENAI-MODEL-OPTIMIZATION", "SOURCE-PHAROS-GUIDE"]
    },
    {
      "id": "CLAIM-LEXICAL-SEMANTIC",
      "statement": "Lexical retrieval such as BM25 and embedding retrieval recover different signals and can be combined when exact terms and semantic similarity both matter.",
      "status": "VERIFIED",
      "sourceIds": ["SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL"]
    },
    {
      "id": "CLAIM-RERANKING",
      "statement": "A reranker can reorder an initial retrieval candidate set before context is passed to the model.",
      "status": "VERIFIED",
      "sourceIds": ["SOURCE-ANTHROPIC-CONTEXTUAL-RETRIEVAL"]
    },
    {
      "id": "CLAIM-CORPUS-QUALITY",
      "statement": "A RAG recommendation presumes an authoritative, permissioned corpus and an operating path for indexing and updates.",
      "status": "BOUNDED",
      "sourceIds": ["SOURCE-MICROSOFT-RAG-VS-FT", "SOURCE-PHAROS-GUIDE"]
    },
    {
      "id": "CLAIM-LORA-METHOD",
      "statement": "LoRA is a parameter-efficient adaptation method that freezes pretrained weights and learns low-rank update matrices.",
      "status": "VERIFIED",
      "sourceIds": ["SOURCE-LORA-PAPER"]
    },
    {
      "id": "CLAIM-HYBRID-PATTERN",
      "statement": "Retrieval and parameter adaptation can be combined when a workload separately requires external domain evidence and specialized model behavior.",
      "status": "BOUNDED",
      "sourceIds": ["SOURCE-RAFT-PAPER", "SOURCE-MICROSOFT-RAG-VS-FT"]
    },
    {
      "id": "CLAIM-RAFT-SCOPE",
      "statement": "RAFT is one retrieval-aware fine-tuning method that trains with relevant documents and distractor documents for domain-specific RAG settings.",
      "status": "VERIFIED",
      "sourceIds": ["SOURCE-RAFT-PAPER"]
    },
    {
      "id": "CLAIM-DUAL-OPERATIONS",
      "statement": "A hybrid design creates both retrieval-index and model-version lifecycle work, so both operating capabilities must be assessed.",
      "status": "BOUNDED",
      "sourceIds": ["SOURCE-RAFT-PAPER", "SOURCE-PHAROS-GUIDE"]
    },
    {
      "id": "CLAIM-PRIVACY-TOPOLOGY",
      "statement": "Privacy and retention properties depend on the selected provider, endpoint, storage path, deployment topology, and configuration; neither architecture has one universal data boundary.",
      "status": "BOUNDED",
      "sourceIds": ["SOURCE-OPENAI-DATA-CONTROLS", "SOURCE-MICROSOFT-RAG-VS-FT"]
    },
    {
      "id": "CLAIM-RUNTIME-MEASUREMENT",
      "statement": "Latency, quality, and cost tradeoffs must be measured on the target workload and deployment rather than inferred from RAG or fine-tuning labels.",
      "status": "BOUNDED",
      "sourceIds": ["SOURCE-MICROSOFT-RAG-VS-FT", "SOURCE-OPENAI-MODEL-OPTIMIZATION"]
    }
  ]
}