{
  "id": 17596687,
  "title": "Build Multi-Tenant RAG in Next.js Without Leaking Customer Data",
  "url": "https://urgent.news/2026/11/01/build-multi-tenant-rag-in-next-js-without-leaking-customer-data",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-11-01T12:25:04.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/vladzoff/build-multi-tenant-rag-in-nextjs-without-leaking-customer-data-52j9"
  },
  "original_language": "en",
  "account": "A key pattern for multi-tenant RAG (retrieval augmented generation) systems is ensuring tenant-level isolation when retrieving relevant document chunks for a user. This prevents potential data leakage where a user might see chunks from other organizations. The critical steps are:\n\n1. Obtain tenant identity from trusted authentication server, not from browser-provided data. The tenant ID should come from a secure server-side check.\n\n2. Store ownership data with each document chunk and maintain a foreign key constraint linking chunks to documents and tenants. This prevents chunks from being assigned to the wrong tenant.\n\n3. Perform tenant filtering in the database query itself, before the retrieved chunks are sent to the LLM model. This enforcement point is critical - it must happen before the model receives any text, otherwise authorization is ineffective.\n\nThe article provides example SQL schema and code snippets in Next.js to implement these tenant isolation checks during document indexing and retrieval. The key takeaway is that proper tenant context must be established in the initial request, and enforced before any data is used in LLM prompts to avoid accidental data disclosure.",
  "summary": "A practical pattern for tenant-scoped retrieval with PostgreSQL, pgvector, row-level security, and integration tests. I've spent about twenty years building software. A pattern worth watching for is a security check that exists in the first request path but gets missed when someone adds a second one. RAG makes that mistake particularly easy: vector search can return a highly relevant passage that…",
  "key_points": [
    "Obtain tenant identity from secure server-side check",
    "Store ownership data with document chunks and foreign key constraint",
    "Perform tenant filtering in database query before sending to LLM"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}