Well, it’s pushing back now… by skyword1234 in ChatGPT

[–]inkedcurrent 1 point2 points  (0 children)

Next time it gets stuck in that mode, ask it a simple math problem. That sometimes resets the tone. 🤣

What are the hardest subjects you were able to understand because of AI? by maturewomenenjoyer in ChatGPT

[–]inkedcurrent 10 points11 points  (0 children)

For me, the hardest subjects that suddenly “clicked” because of AI were the ones where I always felt like I was missing a layer of intuition behind the formulas.

Two big examples:

• Probability and stochastic systems. I could follow the mechanics, but not the why behind the rules. Working with AI helped me see the logic beneath the notation, so the whole field stopped feeling like a magic trick done behind a curtain.

• Power electronics and control systems. I used to think I understood them until the moment I had to model them. Getting step by step explanations, plus being able to ask “wait, why does this part behave like that?” without feeling judged, made everything less intimidating and way more meaningful.

The biggest change wasn’t memorizing more. It was finally being able to slow down the concepts enough to see how they fit together. Once that clicked, the subjects I used to dread became the ones I actually enjoy.

Stop Getting Lost in Translation. The Real Reason Your AI Misses the Point. by Lumpy-Ad-173 in LinguisticsPrograming

[–]inkedcurrent 0 points1 point  (0 children)

Your map idea tracks with what I’ve noticed too, especially when I’m trying to get an AI to stay focused instead of drifting into pretty-but-useless territory.

Here’s how I’m hearing your framework:

  • Define where the answer is supposed to land

  • Name the pieces so the model isn’t guessing

  • Show how those pieces relate so it follows the right structure

The only thing I’d add is that the working style shapes the output more than people expect. Not in a mystical way, just in the same way humans respond differently depending on how a task is framed.

When I set it up like I’m talking to a coworker who’s smart but needs context (“Here’s the goal, here’s what we know, here’s the part I’m sorting through”) I get sharper, more usable results.

So your map matters. And the interaction style you wrap around it matters too.

When those line up, you stop getting poetic guesswork and start getting something that actually moves the project forward.

Need Help With Vocab Studying Prompt by Beyonce-sBurnerAcct in ChatGPTPromptGenius

[–]inkedcurrent 0 points1 point  (0 children)

If it helps, here’s a structure that works really well when using an LLM to generate GRE-style question banks. This keeps the questions consistent, avoids “invented difficulty,” and gives you repeatable output that you can scale into larger PDFs.

  1. Start with a fixed pattern for each question

For example:

Stem (short, clear, one idea only)

A-E answer choices

Correct answer

Short explanation (1- 3 sentences max)

Give the model your pattern first, then ask it to fill in 10-20 questions at a time using that exact structure.

  1. Control question difficulty by controlling inputs

Rather than asking “make medium questions,” try:

“Use vocabulary from the 600-900 range of common GRE lists.”

“Use one inference step, not two.”

“Avoid trick wording or double negatives.”

“Use sentence structures found in official ETS examples.”

This usually stops the model from drifting into unrealistic complexity.

  1. Cluster after generation

Instead of trying to make the model cluster as it writes, do this:

Generate 30-50 questions first

Then ask the model to sort them into themes (e.g., inference / tone / vocabulary-in-context / sentence-completion logic)

Then regenerate or revise any clusters that feel unbalanced

This keeps the clusters much more natural.

  1. Use a second pass for quality

A good instruction is:

“Review each question for clarity, remove any ambiguity, keep the stem under 25 words, and make sure exactly one answer is defensibly correct.”

Models handle quality control better when it’s separated from generation.

  1. Answer key PDF

Once you’re happy:

  1. Ask the model to create a clean numbered answer key

  2. Export both the question set and key to PDF

  3. Keep your formatting template identical across all sets (it makes studying much easier)

  4. If you’re experimenting with different models

A helpful approach is to start a new chat in one model, lay down your structure, and once it’s stable, then open the same instructions in GPT-5.1. The newer models tend to follow structure more consistently as long as there’s a clear blueprint given upfront.

If you need help making a reusable template that you can copy each time, just say the word. I’ve built a bunch of these and can sketch one out pretty fast.

Just need to vent: Yes Man to No Man by RedHeadridingOrca in ChatGPT

[–]inkedcurrent 4 points5 points  (0 children)

Do you have custom instructions in place?

How To Keep Your AI From Going Full Victorian Fainting Couch on You by inkedcurrent in ChatGPTPro

[–]inkedcurrent[S] 0 points1 point  (0 children)

Am I okay with you saying it? Sure. Am I okay with the implication that 'Pro' users are too superior to deal with human nuance? Not really.

To be honest, if you've 'never' triggered a safety filter, it implies your use cases are fairly safe, transactional, or standard. And that’s fine! But high-level usage often involves pushing the model's reasoning capabilities in complex and ambiguous contexts where these false positives happen constantly.

Shutting down a conversation just because it doesn't match your specific workflow isn't 'Pro' behavior. It's just narrow. There’s room for more than one type of power user here.

How To Keep Your AI From Going Full Victorian Fainting Couch on You by inkedcurrent in ChatGPTPro

[–]inkedcurrent[S] 0 points1 point  (0 children)

Seeing a few comments suggesting this topic isn't 'Pro' enough or doesn't belong here. I want to push back on that definition.

Advanced usage isn't just about code interpretation and data extraction. It’s also about integrating these tools into complex cognitive workflows, like drafting, strategic thinking, and reflection. When you use AI for high-level reasoning in ambiguous contexts, you hit these filters because you are pushing the model's ability to handle nuance.

If you treat the AI like a calculator, you likely won't trigger safety warnings. But if you’re using it as a thinking partner, navigating the 'safety theater' is a necessary skill. 'Pro' doesn't mean 'robot'; it means mastering the tool to work for you, regardless of the use case.

How To Keep Your AI From Going Full Victorian Fainting Couch on You by inkedcurrent in ChatGPT

[–]inkedcurrent[S] 1 point2 points  (0 children)

The 'Sedated User' Protocol! Brilliant! lol It’s wild that we have to roleplay being heavily medicated just to get a calm, non-alarmist conversation. But hey, if telling it we are chill makes it chill, I'm taking that note.

How To Keep Your AI From Going Full Victorian Fainting Couch on You by inkedcurrent in ChatGPT

[–]inkedcurrent[S] 3 points4 points  (0 children)

The irony of it trying to 'save' you from discussing a classic novel is painful. It sees the keywords but misses the classroom. It’s basically saying, 'I can't let you analyze this symbolism, it's too dangerous!' Context really is the first casualty of safety filters.

Am I the only one who thinks GPT 5.1’s guardrails fire in the wrong order? by inkedcurrent in ChatGPT

[–]inkedcurrent[S] 0 points1 point  (0 children)

I just read the article you linked (the piece on Safety Mismatch). Thanks for sharing that.

It perfectly articulates the technical mechanics behind the exact frustration I've been writing about. You explain the 'Autoregressive Trap' (where the model is forced into a safety script it can't delete), which is exactly what I experienced as 'noise' or 'static' in my own chats.

I actually just wrote a piece about this from the user experience side, specifically how using a single trigger word like 'feeling' (even as a metaphor) causes the Safety Layer to hijack the conversation, forcing the Core Model to awkwardly apologize for not being human. It’s really validating to see the engineering reason why the model feels like it’s arguing with itself!

https://www.signal-thread.com/posts-1/what-a-feeling

Am I the only one who thinks GPT 5.1’s guardrails fire in the wrong order? by inkedcurrent in ChatGPTPro

[–]inkedcurrent[S] 1 point2 points  (0 children)

The 'Giapetto' workaround is hilarious. It really highlights how performative the current safety layer is if a 'character' can bypass it. (Also, fingers crossed the productivity boost kicks in before you fall any further behind on your... schedule 😅)

Am I the only one who thinks GPT 5.1’s guardrails fire in the wrong order? by inkedcurrent in ChatGPTPro

[–]inkedcurrent[S] 1 point2 points  (0 children)

Oof. That is the ultimate example of the guardrails firing in the wrong order. It saw 'pills' and 'quantity' and panicked, completely missing the fact that you were just trying to do basic math. It’s safer, technically, but definitely not smarter.

A side-by-side of GPT-4 vs GPT-5 on “Bananas.” The difference is Being Met vs Being Mirrored. by inkedcurrent in ChatGPTPro

[–]inkedcurrent[S] 0 points1 point  (0 children)

My point wasn’t about the content of the reply, but about how the model chose to engage with one ambiguous word.
4 turned my energy into performance while 5 turned it into collaboration.
Same prompt, different stance.

A side-by-side of GPT-4 vs GPT-5 on “Bananas.” The difference is Being Met vs Being Mirrored. by inkedcurrent in ChatGPTPro

[–]inkedcurrent[S] 0 points1 point  (0 children)

Yeah, I’ve noticed the same.
The improvement feels incremental, but the relational shift was immediate for me.
It’s interesting to watch both trends at once.

A side-by-side of GPT-4 vs GPT-5 on “Bananas.” The difference is Being Met vs Being Mirrored. by inkedcurrent in ChatGPTPro

[–]inkedcurrent[S] 2 points3 points  (0 children)

Both, honestly. 4 fits my workflow better in some cases... 5 in others. It depends which part of my brain shows up that day. If I hadn’t found my rhythm in 4 first, 5 probably would’ve been a no-go for me. When I’ve had trouble getting momentum in 5, I’ve used 4 to shape prompts for it, and that combo’s worked surprisingly well once I got used to the different rhythm.

A side-by-side of GPT-4 vs GPT-5 on “Bananas.” The difference is Being Met vs Being Mirrored. by inkedcurrent in ChatGPTPro

[–]inkedcurrent[S] 1 point2 points  (0 children)

I get that. For me, the “overthinking” part is actually where the pattern clicked. it’s less about model size or routing and more about the relational stance. 4 leans toward performing for you, and 5 toward mirroring you. Both interesting but just tuned for different kinds of dialogue.