Start by treating the AI like a helpful analyst who must show its work. The more you ask for verifiable grounding—sources, quotes, timestamps, and uncertainty—the less room there is for made-up details. A good explanation request balances clarity (what you need) with constraints (what counts as acceptable evidence).
When you want an explanation, require the assistant to cite where each key claim comes from. Ask it to separate “what the sources say” from “my interpretation” and to include direct quotations for critical points. If you’re working from documents, tell it to only use those materials and to flag anything not supported by them.
Hallucinations often appear when the assistant tries to fill gaps. Prevent that by stating boundaries: the timeframe, geography, product category, dataset, or authors you care about. Also give permission to say “not enough information,” and request a short list of what would be needed to answer confidently (missing citation, missing metric, missing definition).
Ask for step-by-step logic that references the evidence at each step. If the topic involves numbers, request the intermediate calculations and units. If it involves scientific or historical claims, request the specific study or primary source and the relevant passage. If it can’t provide those, the assistant should downgrade confidence and avoid specifics.
Before accepting the explanation, ask the assistant to run a self-audit: list the top 3 claims most likely to be wrong, provide alternative interpretations, and identify any assumptions. For a practical framework to get clearer, source-backed summaries and explanations, see the guide here: https://superboffersarea.shop/blog/guide-ai-supported-research-system-summaries-explanations/.
Ask it to restate the answer with citations or direct quotes for each major claim, and to mark any unsupported parts as uncertain. If it can’t cite evidence, treat the content as unverified and request a narrower, document-only response.
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