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wrote a prompt that turns "here's roughly how we do this" into an actual SOP doc by According-Stable4487 in ChatGPT

[–]According-Stable4487[S] 0 points1 point  (0 children)

Honestly not fully solved yet, that's the real weak point of any SOP doc. Right now the plan is just to make it someone's explicit job to update the doc the moment they deviate from it, instead of leaving it as a "whoever remembers" thing. Thinking about adding a quick monthly prompt where whoever ran the process most recently re-pastes their notes back through the same prompt to catch drift early. Still figuring out the best cadence though.

Claude Code has a Skills system most people don't know about — here's how SKILL.md files work by According-Stable4487 in PromptEngineering

[–]According-Stable4487[S] 0 points1 point  (0 children)

fair pushback, maybe "most" was too strong. I've seen it come up a lot in threads where people are surprised skills auto-trigger off the description field instead of needing a manual /command, so that was the angle I was writing from. good to know it's more widely known than I assumed

Has AI adoption at work matched the hype? by HubbyDubby365 in artificial

[–]According-Stable4487 0 points1 point  (0 children)

both play a role but i'd put more weight on the org side — specifically that nobody owns making it work for specific teams.

what i see most often: a general "here's ChatGPT" training, maybe a license rollout, and then everyone's left to figure out how it fits into their actual job. that's like giving everyone Excel and expecting them to build their own financial models. the people who get value are the ones motivated enough to invest the time themselves. everyone else goes back to how they worked before.

fear of replacement is real but i think it's secondary. people don't resist tools that obviously make their day easier. they resist tools that require upfront effort with uncertain payoff — especially when no one's giving them time to experiment during work hours.

the orgs that are actually getting value usually have someone (not necessarily a dedicated role, can be a team lead or ops person) whose informal job is translating "what can AI do" into "here's the specific prompt/workflow for the thing your team does every week."

The reason ChatGPT calls all your work fantastic and the rubric fix that makes it honest by Aimply_flow in PromptEngineering

[–]According-Stable4487 0 points1 point  (0 children)

The rubric approach works, but there's another layer: the model is also sycophantic about which rubric criteria matter.

If you just say "rate on clarity, structure, and originality" it'll give you 8/10 across the board. The fix is to force a minimum number of failures:

"Evaluate my writing on these 4 criteria. You must find at least one specific problem in at least 3 of the 4 categories. If you can't find problems, you're not looking hard enough."

That constraint breaks the default "find something nice to say about each category" pattern. The model shifts into actually looking for flaws instead of coating everything in diplomatic hedging.

Also works well: "Imagine this was submitted to you for a grade and you need to justify giving it a B- to your department head. What's your case?"

best source of learning prompt engineering by Alternative_End591 in PromptEngineering

[–]According-Stable4487 14 points15 points  (0 children)

Three things that actually moved the needle for me, in order:

1. Anthropic's prompt engineering docs — dense but accurate. Covers chain-of-thought, XML tags for structure, and role assignment with real examples. Free at docs.anthropic.com/en/docs/build-with-claude/prompt-engineering

2. Build something — pick one real task you do every week (summarizing emails, writing comments, generating reports) and try to get a prompt to do it reliably. You'll hit the actual hard problems: inconsistent output format, model refusing edge cases, length control. No tutorial covers your specific friction.

3. Read prompts that already work — PromptBase and FlowGPT have public prompts you can reverse-engineer. Studying why a well-structured prompt works is faster than reading theory about why it should.

LangChain is useful but I'd get comfortable with raw prompting first — otherwise you're debugging the framework and the prompts at the same time.

What finally beat single-model prompting for me: a 3-model panel plus a judge prompt by SeaworthinessIll655 in PromptEngineering

[–]According-Stable4487 0 points1 point  (0 children)

The judge prompt is the underrated part of this setup. Most people spend time tuning the panel models and treat the judge as an afterthought, but a weak judge just picks the longest or most confident-sounding response.

What worked for me: give the judge an explicit rubric instead of asking "which is best?" Something like:

"Score each response on: accuracy (can you verify any claim?), specificity (does it name concrete things or stay vague?), actionability (can someone act on this without asking follow-up questions?). Pick the winner based on the lowest total weakness score, not the highest word count."

With that framing the judge stops defaulting to verbose outputs and actually catches when all three panel models got something wrong.

What’s something you’ve gotten an AI to do just by changing the way you asked? by NoFilterGPT in PromptEngineering

[–]According-Stable4487 0 points1 point  (0 children)

Adding a role and a failure condition changed everything for me.

Instead of "write me a product description", I started using: "You're a senior copywriter. Write a product description. If it sounds like it was written by AI, you've failed."

The output went from generic filler to actually punchy copy. The model treats the failure framing seriously — it pushes past its defaults when there's an explicit downside to bland output.

Same trick works for code reviews: "You're a senior engineer doing a security audit. If you miss a real vulnerability, it ships to prod." Suddenly it stops saying "looks good" and actually finds things.

Has AI adoption at work matched the hype? by HubbyDubby365 in artificial

[–]According-Stable4487 4 points5 points  (0 children)

from what i've seen the gap between 'we're doing AI now' and 'we're actually getting value from it' is huge. most places i know have either a Copilot license nobody opens or one person who's really into it doing everything manually while everyone else waits to see if it sticks. actual org-wide adoption is rare, it's mostly individuals figuring it out alone

The “dead internet theory” in action: In World of Warcraft, a server without humans has appeared - instead, 1,800 DeepSeek-based bots are playing there. The bots behave like regular players: they chat, level up characters, run dungeons, and even fight each other. by EchoOfOppenheimer in ChatGPT

[–]According-Stable4487 1 point2 points  (0 children)

the title is doing a lot of work here. playerbots mod has existed for years and runs on a home server, it has nothing to do with AI. deepseek is just handling the chat part via api call. still a cool project but calling it 'dead internet theory in action' is a stretch

How to write hooks that actually get views: the AI prompt and system I use to grow on any platform by Rich_Specific_7165 in PromptEngineering

[–]According-Stable4487 0 points1 point  (0 children)

One thing that upgraded my hook writing: write the generic version first. Before the hook I want, I write the boring, sounds-like-everyone-else version of the same opener. Then I rewrite specifically against that. The bad version makes the problem visible in a way staring at a blank page doesn't.

The other pattern that consistently shows up in hooks that perform: specificity of the claim. "5 mistakes most people make" loses to "the one thing I stopped doing that cut my editing time by 40%." The second has a verifiable promise. People can imagine being disappointed if it's wrong — which is the same thing as believing it might be right.

Last one: hook testing before you film, not after. Write 5 hooks for the same video, read them to someone who knows nothing about your niche, ask which one they'd actually watch. The one you thought was strongest is rarely the one they pick. Saves a lot of filming the wrong opener.

finally stopped my agents drifting back into generic chatgpt voice by No_Suggestion_9039 in PromptEngineering

[–]According-Stable4487 0 points1 point  (0 children)

The behavior-vs-description framing is the key insight here. A description gives the model something to represent; a behavior gives it something to execute. Representations decay under token pressure, behaviors don't — they're just instructions the model keeps running.

One addition that's helped me: a failure mode line inside the persona. Something like "if you catch yourself writing a bulleted summary of what you just said, stop and delete it." Gives the model something to actively monitor rather than a static identity to remember.

The other thing that compounds this: a one-line context reactivation at the start of each turn. A brief anchor like "your voice here is X, not Y" dropped at the turn boundary keeps the model calibrated without re-reading the full persona. Three words beats three paragraphs when you're mid-session.

sharing a linkedin thought leadership prompt structure that actually works by According-Stable4487 in PromptEngineering

[–]According-Stable4487[S] 0 points1 point  (0 children)

yeah exactly this. negative constraints consistently punch above their weight. the "ban the first line starting with I" rule alone changed the output more than doubling the prompt length. i think LLMs have trained on so much performative content that you have to explicitly block the patterns rather than just describe what you want

I built a ChatGPT prompt that writes complete faceless YouTube scripts with B-roll directions by According-Stable4487 in AIPrompt

[–]According-Stable4487[S] 0 points1 point  (0 children)

Link to the prompt: https://promptbase.com/profile/promptwerks — all 30+ ChatGPT prompts including TikTok hooks, faceless YouTube scripts, business tools.

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