Which of the following are recommended directives for a production RAG system prompt to maximize grounding? Select all that apply.
The grounding triad for a RAG system prompt: answer from context only, cite chunks by number, and refuse when context is insufficient. Tone and chunk placement are unrelated.
Imagine a librarian who hands you a stack of pages and asks you to answer a customer's question. You give the librarian three rules. First: answer only from these pages, do not use what you already know. Second: after each sentence, write which page it came from so anyone can check. Third: if the pages do not actually contain the answer, just say so instead of making something up. Those three rules are exactly what a RAG system prompt tells the language model. They keep the answer honest, traceable, and safe. Being friendly, or whether the pages sit in one envelope or another, does not change whether the librarian sticks to the pages. That is why only the first three rules belong to the grounding set.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
A common misconception is that RAG works the moment you paste retrieved chunks into the prompt. Retrieval gets the right evidence in front of the model, but it does nothing to change how the model treats that evidence. By default a language model regards context as one more hint and will happily blend in whatever it remembers from training, hedge when the context is thin, or answer confidently from chunks that do not actually support the claim. The system prompt is where you close that gap.
This question isolates the three directives that do the closing, the grounding triad: answer from context only, cite each claim by chunk, and refuse when the context is insufficient. The three distractors are all legitimate prompt-engineering moves, but each addresses a different concern, so the exercise is really about recognizing what grounding means and what it does not. The rest of this walkthrough takes each directive, the failure it prevents, and the metric it moves, then explains why tone, message placement, and domain scope sit outside the set.
Directive one: answer from context only
The ground only directive tells the model to construct its answer from the provided chunks and not to fall back on parametric memory. This sounds redundant once the chunks are in the prompt, but it is not. A model with no such instruction treats the retrieved text as supporting material around a question it already thinks it can answer.
The consequence is subtle and dangerous. On a query where the chunks are correct, the model may still inject a training data fact that is stale or wrong, and you get a partially hallucinated answer even though retrieval did its job. This is exactly the failure RAG exists to remove, so reintroducing it through a missing directive defeats the architecture.
In evaluation terms, ground only is what your faithfulness metric measures: the fraction of claims in the answer that are actually entailed by the retrieved context. A weak or absent ground only directive shows up as faithfulness scores that drop even when retrieval recall is high.
Wording matters more than people expect. 'Use the context to answer' is too soft; the model reads it as permission, not restriction. The stronger forms set an explicit precedence rule, that the context overrides prior knowledge, and an explicit prohibition on outside facts. For high stakes domains some teams go further and add a self-check instruction, asking the model to re-read its draft answer and strike any sentence not supported by a chunk before returning it.
Situations where this technique stops working.
2–4 min · Everything important, quickly.
You are a question answering assistant. Use ONLY the information in the
numbered context chunks below to answer. Do not use prior knowledge.
RULES:
1. Answer strictly from the provided context.
2. After each claim, cite its source as [chunk_number], e.g. [2].
3. If the context does not contain enough information to answer,
reply: "I don't have enough information to answer that."
CONTEXT:
[1] {chunk_1}
[2] {chunk_2}
[3] {chunk_3}
QUESTION: {user_query}| Directive | Failure it prevents | Metric it moves |
|---|---|---|
| Ground-only | Blending training data facts into the answer | Faithfulness |
| Cite-chunks | Unverifiable, unattributable claims | Attribution / auditability |
| Refuse-if-insufficient | Confident answers from a retrieval miss | Abstention / trust |
| Friendly tone (distractor) | Nothing about grounding | UX, not faithfulness |
Real products, models, and research that use this idea.
- Perplexity 2026 answers cite numbered sources inline and decline cleanly when its retrieval surfaces no supporting pages.
- Anthropic's Claude with the Citations API emits chunk-level source references, the cite chunks directive enforced at the API level.
What an interviewer would ask next. Try answering before peeking at the approach.
QHow do you verify the model actually obeys the cite chunks directive instead of fabricating citation tags?
Parse emitted [n] tags, check each maps to a retrieved chunk, and run a faithfulness eval confirming the cited chunk actually supports the claim. Penalize uncited or mismatched claims.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Selecting the tone or message placement options. Those are UX or architecture choices; they do not change whether the model grounds its answer in the retrieved context.
60 second bullets to scan on the way to the call.
The three grounding directives and the one job each one does
Why ground only is needed even when the chunks are already in the prompt
Primary sources. Browse if you want the original framing.
Same topic, related formats. Practice these next.