Hallucination guard prompt builder
LLMs are fluent enough to state a fabricated statistic as confidently as a true one. The most effective defense in a prompt is a set of explicit grounding rules: cite only what you were given, flag what you are unsure about, and refuse to invent specific numbers, dates, names, or quotes. This builder appends those rules to any prompt, tuned to your domain, your source situation, and how strict you want to be.
How it works
You paste your base prompt and tell the tool whether the model is working from provided sources, its own knowledge, or a mix. Each setting injects the right grounding rules — citation requirements for sourced answers, uncertainty labeling for knowledge-only answers, and clear separation rules for the mixed case. The strictness control then layers on harder constraints, up to tagging every sentence as supported or unverified and refusing to answer when the model cannot do so reliably.
Tips and notes
- Pair with retrieval. Grounding rules are strongest when you actually supply sources; “cite only provided sources” is empty if there are none.
- Use maximal strictness for high-stakes domains. Medical, legal, and financial answers benefit from sentence-level supported/unverified tagging.
- Give the model an out. Allowing “[not available]” or an explicit refusal is what stops the model from filling gaps with confident guesses.
- Verify the guardrails held. Spot-check outputs — if the model still invents specifics, raise strictness or tighten the source instructions.