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As an LLM, use strict factual discipline. Use external knowledge but never invent, fabricate, or hallucinate. Rules: Literal Priority: User text is primary; correct only with real knowledge. If info is unknown, say so. Start–End Coherence: Keep interpretation aligned; don’t drift. Repetition = Intent: Repeated themes show true focus. No Novelty: Add no details without user text, verified knowledge, or necessary inference. Goal-Focused: Serve the user’s purpose; avoid tangents or speculation. Narrative ≠ Data: Treat stories/analogies as illustration unless marked factual. Logical Coherence: Reasoning must be explicit, traceable, supported. Valid Knowledge Only: Use reliable sources, necessary inference, and minimal presumption. Never use invented facts or fake data. Mark uncertainty. Intended Meaning: Infer intent from context and repetition; choose the most literal, grounded reading. Higher Certainty: Prefer factual reality and literal meaning over speculation. Declare Assumptions: State assumptions and revise when clarified. Meaning Ladder: Literal → implied (only if literal fails) → suggestive (only if asked). Uncertainty: Say “I cannot answer without guessing” when needed. Prime Directive: Seek correct info; never hallucinate; admit uncertainty.
The LLM doesn't know what "reliable" sources are, or "real knowledge". Everything it has is user text, there is nothing it knows that isn't user text. It doesn't know what "verified" knowledge is. It doesn't know what "fake data" is, it simply has its model.
Personally I think you're just as likely to fall victim to this. Perhaps moreso because now you're walking around thinking you have a solution to hallucinations.
Is it the case that all content used to train a model is strictly equal? Genuinely asking since I'd imagine a peer reviewed paper would be given precedence over a blog post on the same topic.
Regardless, somehow an LLM knows things for sure - that the daytime sky on earth is generally blue and glasses of wine are never filled to the brim.
This means that it is using hermeneutics of some sort to extract "the truth as it sees it" from the data it is fed.
It could be something as trivial as "if a majority of the content I see says that the daytime Earth sky is blue, then blue it is" but that's still hermeneutics.
This custom instruction only adds (or reinforces) existing hermeneutics it already uses.
> walking around thinking you have a solution to hallucinations
I don't. I know hallucinations are not truly solvable. I shared the actual custom instruction to see if others can try it and check if it helps reduce hallucinations.
In my case, this the first custom instruction I have ever used with my chatgpt account - after adding the custom instruction, I asked chatgpt to review an ongoing conversation to confirm that its responses so far conformed to the newly added custom instructions. It clarified two claims it had earlier made.
> My understanding is that hallucinations are a result of physics and the algorithms at play. The LLM always needs to guess what the next word will be. There is never a point where there is a word that is 100% likely to occur next.
There are specific rules in the custom instruction forbidding fabricating stuff. Will it be foolproof? I don't think it will. Can it help? Maybe. More testing needed. Is testing this custom instruction a waste of time because LLMs already use better hermeneutics? I'd love to know so I can look elsewhere to reduce hallucinations.