Which of the following are recommended directives for a production RAG system prompt to maximize grounding? Select all that apply.

A RAG pipeline fetches documents and pastes them into the prompt, but pasting context in is not enough. The system prompt has to tell the model how to use that context. Three directives form the canonical **grounding** set. First, ground only: answer using just the provided context, not training data knowledge. Without this, the model treats the chunks as a hint and blends in half remembered facts, which reintroduces hallucination. Second, cite chunks: tag each claim with its source chunk number, like [3]. This makes answers verifiable for the user and lets an eval harness check attribution automatically. Third, refuse on insufficient context: if the chunks do not contain the answer, say so instead of guessing. This is the safety valve for the common case where retrieval misses. The other options, a friendly tone, putting chunks in the user message, or restricting topics, are real prompt-engineering choices, but none of them changes whether the model stays grounded. That is why only the first three are correct.

Retrieval only solves half the problem. Once the chunks are in the prompt, the system prompt has to constrain how the model uses them. Three directives do exactly that. **Ground-only** tells the model to answer from the provided context and not fall back on parametric knowledge. Skip it and the model treats context as a suggestion, blending in training data facts and reintroducing the hallucination RAG was meant to remove. Cite-chunks requires each claim to carry its source chunk number, like [3]. This buys you verifiable, attributable answers and, just as important, gives an eval harness a machine-checkable signal for faithfulness. Refuse-if-insufficient is the safety valve: when retrieval misses and the chunks do not contain the answer, the model must say so rather than fabricate. This is the single most common production failure, the model confidently answering from irrelevant context. The distractors are all real techniques addressing different concerns. Tone is a UX choice orthogonal to faithfulness. Whether chunks live in the system or user message is a minor placement detail modern models handle either way. Domain restriction governs scope, not grounding. None of the three changes whether the answer is grounded in the retrieved evidence.