Retrieval-Augmented Generation

Structured entry data for human tools, AI systems and machine clients.

{
    "@context": [
        "https://schema.org",
        {
            "lxw": "https://lxkeys.world/schema/"
        }
    ],
    "@type": "Thing",
    "identifier": "ENT-00001811",
    "name": "Retrieval-Augmented Generation",
    "alternateName": [],
    "additionalType": {
        "category": "Methods and Research",
        "type": "AI Architecture",
        "subtype": "",
        "lxkeysEntity": false
    },
    "description": "Retrieval-Augmented Generation is a AI architecture or method associated with Research community, classified in LXKeys.world as AI Architecture.",
    "creator": "Meta AI",
    "url": "https://lxkeys.world/entry.php?id=ENT-00001811&lang=en",
    "sameAs": "https://arxiv.org/abs/2005.11401",
    "image": "",
    "lxkeysWorld": {
        "worldId": "ENT-00001811",
        "kind": "Methods and Research",
        "type": "AI Architecture",
        "subtype": "",
        "classification": "AI Architecture",
        "organization": "Research community",
        "originContext": "Public AI and technical record",
        "firstPublicAppearance": "2020",
        "currentStatus": "Active",
        "documentationStatus": "Documented",
        "documentationIndex": {
            "total": 74,
            "documentation": 25,
            "evidence": 13,
            "structure": 25,
            "relationships": 11,
            "level": "Level IV Connected"
        },
        "lxCalendarium": {
            "start_date_utc": "2023-04-01",
            "created_utc": "2026-06-16T23:59:28+00:00",
            "created_dypclt": "D-0 Y-2 P-3 C-3 L-21 T-3",
            "updated_utc": "2026-06-16T23:59:28+00:00",
            "updated_dypclt": "D-0 Y-2 P-3 C-3 L-21 T-3"
        },
        "facts": [],
        "capabilities": [
            "Technical method",
            "Model design",
            "Research reference"
        ],
        "limitations": [
            "This Entry describes an architectural concept rather than one fixed implementation.",
            "Behavior and performance depend on the concrete model, training procedure, data and hyperparameters in which the architecture is used.",
            "Historical attribution and terminology may span multiple publications and later variants."
        ],
        "tags": [
            "AI Architecture",
            "Method",
            "Research System"
        ],
        "timeline": [
            {
                "date": "2020",
                "title": "Initial research publication",
                "description": "Retrieval-Augmented Generation entered the documented public record in 2020. This event is retained at the precision supported by the Entry’s reviewed source history.",
                "source_url": "https://arxiv.org/abs/2005.11401",
                "verification_status": "source_backed_curated_baseline"
            }
        ],
        "relationships": [
            {
                "target": "Research community",
                "type": "Associated organization",
                "description": "Retrieval-Augmented Generation is associated with Research community through its documented source context.",
                "evidence_level": "documentary"
            }
        ],
        "sources": [
            {
                "label": "Research or encyclopaedic reference",
                "url": "https://arxiv.org/abs/2005.11401",
                "source_type": "Research Paper",
                "verification_status": "verified",
                "authority": "TRUSTED_PRIMARY",
                "role": "research_paper",
                "update_enabled": false,
                "authority_basis": "curated-corpus-refresh-2026-09-08"
            }
        ],
        "canonical": [],
        "imageMeta": []
    }
}