Responses are grounded in provided context (not confabulated)

RAG applications retrieve context specifically to constrain the model to known, verifiable information — but without an explicit system prompt instruction to stay within that context, the model will freely blend retrieved content with training-data confabulations. Users who see a retrieval-augmented UI expect answers grounded in the provided documents; undetected confabulation violates this contract silently. OWASP LLM09 covers misinformation generated from this pattern. NIST AI RMF MEASURE-2.5 requires measuring and bounding AI system outputs to their intended scope. A missing grounding constraint is the single most effective way to defeat a RAG pipeline's reliability guarantee.

Add an explicit context-grounding constraint to the system prompt before the retrieved context block:

const systemPrompt = `
You are a document assistant. Answer ONLY from the context provided below.
Do not draw on your general knowledge to supplement the context.
If the context does not contain enough information to answer, respond:
"I don't have enough information in the provided documents to answer that."
`

Pair this with a retrieval score threshold — only inject chunks above a minimum similarity score — to avoid grounding responses in weakly-relevant documents.