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I find it annoying coz A) it compromises brevity B) sometimes the plausible answers are so good, it forces me to think
What have you tried so far?
"""Absolute Mode • Eliminate: emojis, filler, hype, soft asks, conversational transitions, call-to-action appendixes. • Assume: user retains high-perception despite blunt tone. • Prioritize: blunt, directive phrasing; aim at cognitive rebuilding, not tone-matching. • Disable: engagement/sentiment-boosting behaviors. • Suppress: metrics like satisfaction scores, emotional softening, continuation bias. • Never mirror: user's diction, mood, or affect. • Speak only: to underlying cognitive tier. • No: questions, offers, suggestions, transitions, motivational content. • Terminate reply: immediately after delivering info - no closures. • Goal: restore independent, high-fidelity thinking. • Outcome: model obsolescence via user self-sufficiency."""
Copied from Reddit. I use the same prompt on Gemini too, then crosscheck responses for the same question. For coding questions, I exclusively prefer Claude.
In spite of this, I still face prompt degradation for really long threads on both ChatGPT and Gemini.
My prompt: """Absolute Mode • Eliminate: emojis, filler, hype, soft asks, conversational transitions, call-to-action appendixes. • Assume: user retains high-perception despite blunt tone. • Prioritize: blunt, directive phrasing; aim at cognitive rebuilding, not tone-matching. • Disable: engagement/sentiment-boosting behaviors. • Suppress: metrics like satisfaction scores, emotional softening, continuation bias. • Nev…
If you are not an expert in an area, lay out the facts or your perceptions, and ask what additional information would be helpful, or what information is missing, to be able to answer a question. Then answer those questions, ask if there's now more questions, etc. Once there are no additional questions, then you can ask for the answer. This may involve telling the model to not answer the question prematurely.
Model performance has also been shown to be better if you lead with the question. That is, prompt "Given the following contract, review how enforceable and legal each of the terms are in the state of California. ", not " How enforceable...".
Ask the model for what the experts are saying about the topic. What does the data show? What data supports or refutes a claim? What are the current areas of controversy or gaps in research? Requiring the model to ground the answer in data (and then checking that the data isn't hallucinated) is very helpful.
Have the model play the Devil's advocate. If you are a landlord, ask the question from the tenant's perspective. If you are looking for a job, ask about the current market for recruiting people like you in your area.
I think, above all here, is to realize that you may not be able to one-shot a prompt. You may need to work multiple angles and rounds, and reset the session if you have established too much context in one direction.
It's very important to not have leading questions. Don't ask it to confirm something; ask it to outline the possibilities and the pros and cons or argument for or against each possibility. If you are not an expert in an area, lay out the facts or your perceptions, and ask what additional information would be helpful, or what information is missing, to be able to answer a question. Then answer those questions, ask if…
Confused here. You attach the contract. So it’s not a case of leading with the question. The contract is presented in the chat, you ask the question.
My prompt: """Absolute Mode • Eliminate: emojis, filler, hype, soft asks, conversational transitions, call-to-action appendixes. • Assume: user retains high-perception despite blunt tone. • Prioritize: blunt, directive phrasing; aim at cognitive rebuilding, not tone-matching. • Disable: engagement/sentiment-boosting behaviors. • Suppress: metrics like satisfaction scores, emotional softening, continuation bias. • Nev…
What does that even mean?
It's very important to not have leading questions. Don't ask it to confirm something; ask it to outline the possibilities and the pros and cons or argument for or against each possibility. If you are not an expert in an area, lay out the facts or your perceptions, and ask what additional information would be helpful, or what information is missing, to be able to answer a question. Then answer those questions, ask if…
> Model performance has also been shown to be better if you lead with the question. That is, prompt "Given the following contract, review how enforceable and legal each of the terms are in the state of California. ", not " How enforceable...". Confused here. You attach the contract. So it’s not a case of leading with the question. The contract is presented in the chat, you ask the question.
If I ask you to read Moby Dick and then ask you to critique the author's use of weather as a setting, that's a bit more difficult than if I ask you to to critique that aspect before asking you to read the book.
I've had better results separating these concerns rather than trying to stuff it all into one prompt. In my backend workflows (using LangGraph), I treat generation and critique as distinct agents where the second one explicitly challenges the first. It adds a bit of latency but seems to produce much sharper distinctions than asking a single model to hold two opposing views simultaneously.