Enterprise RAG

Demo RAG works on a single PDF; enterprise RAG must serve thousands of users across HR, legal, and engineering docs without leaking data, serving stale policies, or hallucinating when retrieval fails.

A corporate librarian who checks your badge before handing you folders, stamps documents with revision dates, and refuses to guess when the shelf is empty.

Scenario

A sales rep asks 'What's our discount policy for renewals over $500K?' The agent retrieves only deal-desk docs their role can access, cites the Q3 policy PDF page 4, and refuses to answer if no authorized chunk exceeds the relevance threshold.

Solution

In Enterprise AI, apply Enterprise RAG to this scenario: A sales rep asks 'What's our discount policy for renewals over $500K?' The agent retrieves only deal-desk docs their role can access, cites the Q3 policy PDF page 4, and refuses to answer if no authorized chunk exceeds the relevance threshold. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to Enterprise RAG (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where Enterprise RAG happens in the code.

1async function enterpriseRagQuery(user, question) {  # key line for Enterprise RAG2  const allowedSources = await acl.resolveSources(user.id, user.roles);3  const chunks = await hybridSearch(question, {4    filter: { tenant_id: user.tenant_id, source_id: { $in: allowedSources } },5    k: 12,6  });7  const reranked = await reranker.rank(question, chunks).slice(0, 5);8  if (reranked[0].score < 0.72) return { answer: null, reason: "insufficient_grounding" };  # return the result9  return llm.generate({ question, context: reranked, requireCitations: true });  # return the result10}

• Treating Enterprise RAG as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for enterprise rag

Quick recap — the most important points from this module.