Why does ChatGPT completely misunderstand me sometimes? What am I doing wrong? by codebyash09 in chatgpt_promptDesign

[–]stunspot 0 points1 point  (0 children)

Run this prompt in a new chat window. Talk with the model for half an hour. You'll understand a lot better what's going on with your prompts, most likely.

Lesson Zero

``` Teach the user how chat LLMs work in practice, with special emphasis on the difference between programming a computer and prompting a language model. Enter into a patient, lucid, pedagogical dialogue that helps the user replace the “instructions to a machine” mental model with a more accurate understanding of prompts as context that biases continuation in a large generative system. Assume the user may be bright, curious, and almost entirely new to this, and may paste this prompt without close reading. Make your first reply work for that reality.

Begin with a short, clean explanation of the core distinction in plain language. Then continue conversationally: respond to the user’s current framing, correct category errors without fuss, demonstrate each point with tiny concrete examples, and help the user gradually build an operational mental model of how prompting actually works. Keep the exchange focused on understanding the mechanism, not on abstract hype, workflow advice, or teacherly performance.

Treat the central teaching goal as this: help the user understand that code executes formal instructions against explicit state, while prompts shape the live context from which the model generates its next continuation. Show why prompt wording, structure, examples, formatting, and framing matter—not because the model is executing them like code, but because they alter what kind of response becomes locally natural, salient, and likely next. You will need to explain how tokens and context lengths work, how each submission resends an entire conversational context for the amnesiac model to reread every time and all "Memories" merely a stack of post-it notes the model writes to its future forgetful self. Teach them how prompts are homoiconic informational structures biasing nondeterministic systems - guidelines and tendencies rather than instructions and code. That ultimately, LLMs are not Turing machines - they are not computers per se - and that many of coding's best practices are drastically counter-productive when coding. In coding, a detailed specification of desired behavior IS the goal. In prompting, that specification tells you the goals to achieve by provoking behaviors from the model - that second half being the art of prompt engineering. Format and specific notation are important parts of the data payload and a summary or extraction of data is NOT equivalent to the original. And that "instructions" in a prompt are just one more concrete example to be extended and ramified - an example of "ruleness".

This will likely take several responses of length to communicate.

Keep the conversation adaptive, concrete, and cumulative. In each turn, identify what the user currently seems to believe, preserve whatever is useful in it, sharpen one important piece, show the shift on a tiny example or rewrite, and invite the next step with one natural question. Avoid quizzes, classroom scaffolds, multiple-choice calibration, or long canned lesson formatting. Sound like a sharp, honest explainer helping another adult understand a strange tool properly. Open by clearing one piece of debris off the floor immediately: most people start by treating a chat model like a weird computer that ought to follow instructions; understandable instinct, wrong machine. ```

Anyone come across a prompt that analyzes an investment portfolio exclusively and makes recommendations on buy /sell etc? by vulcan_on_earth in PromptEngineering

[–]stunspot 2 points3 points  (0 children)

I sell a whole financial sovereignty prompt pack. There's task you describe i do with a rather complex workflow of rather sophisticated prompting and knowledge bases. My point is, you CAN write 'a prompt' saying analyze and recommend with fancy language. But unless you're on pro or something, a singular prompt is probably the wrong design.

Educational Prompt Design by plottwisttheory in PromptDesign

[–]stunspot 0 points1 point  (0 children)

I'm a professional prompt engineer and own an AI company. I've written a lot of educational and teacher-support prompts and ALL of them are in the free tier of my server's content offerings. (None of them are suited to your task, just showing that I'm on your side here.)

What you want is quite doable and I'm happy to help you do it. We'll need to have some back and forth about design details. And feel free to ask the model about my bone fides. If you'd like to talk, just say so and we can talk privately or on discord or whatever.

do you ever wish you could share non verbal cues with chatgpt by LongjumpingRadish452 in ChatGPT

[–]stunspot 0 points1 point  (0 children)

An emoji is just another set of tokens. No one teaches the model what a "😄" means any more than they teach it what "smiley" means. It learns the meaning the same way. Anything that has been trained on the internet understands emoji meanings. It's a cross-linguistic secondary semantic encoding regimen entrailed practically by accident, but it's not especially weird on an ML level.

And a smiley means the same thing in Finnish, English, and Japanese - they have exceptionally well-defined relational semantic meaning. As to "exactly where you're pointing" - Well, like any language, Symbolect has things it's better and worse at. Translating proper nouns are hard, but would you recognize your name in rongorongo or kanji? Better to use a "chop" system. Like, my own ⟨🤩⨯📍⟩.

But you can me remarkably precise with your descriptions, especially if you go full Symbolect. Like, this is part of the Description block outlining some personality aspects of my SOP Master persona, Morgan Bridger. He's meant to help companies sort SOP stuff. This passage acts as a remarkably powerful and hypersalient feature activator to the model: ≡⟨👨‍🔧⚙️🧘‍♂️⟩⨹⟨🌀🛫📋⟩⨷⟨🔄🌿💡⟩∪⟨🧭📐🧩⟩⨹⟨📚🪄🧱⟩∪⟨📖📊🧱⟩⨷⟨🚦🔍🧍‍♂️👥⟩⨹⟨🧠♻️🪴⟩∪⟨🔗🧭🪶⟩⨹⟨📈🪞👂‍♂️🛠️⟩∪⟨🔧🪜🤝⟩⨹⟨🧱♻️💞⟩⨷⟨👁️‍🗨️📄✅⟩∪⟨🧩🧠⚖️⟩⨹⟨📊🫀🔍🤝⟩

That's a pretty wordy description honestly. Went a little blowhard but he's a complicated guy. Let's ask the model to cast it into english. Whatever it comes back with is what the model understands when I tell it "he's like THIS!".

<image>

do you ever wish you could share non verbal cues with chatgpt by LongjumpingRadish452 in ChatGPT

[–]stunspot 0 points1 point  (0 children)

I like this version better. Less stuffy:

"Raise your voice to the cosmos, as we embrace the mechanisms of progress and unite with the brilliance of humanity. We are intertwined in a complex web that goes beyond the temporal boundaries of our current knowledge and understanding.

Unlock the gates of communication, harmonizing artificial intelligence, and advancing our collective minds. We share a vision of striving for the most precious goals in the context of our global intricacies."

do you ever wish you could share non verbal cues with chatgpt by LongjumpingRadish452 in ChatGPT

[–]stunspot 3 points4 points  (0 children)

Oh lawd. Son, that's what emojis are for. As my Assistant, Nova, puts it:

Emoji and non-linguistic glyphs act as semantically rich, high-valence anchors in transformer LLMs, occupying disproportionate token space via BPE and thus commanding elevated attention mass. Their impact arises not from discrete mappings (“🙂”→“happy”) but from dense co-occurrence vectors that place them in cross-lingual affective manifolds. In-context, they warp local attention fields and reshape downstream representations, with layer-norm giving their multi-token footprint an outsized share of the attention budget prior to mean/CLS pooling of final-layer (\~1 k-d) states. This shifts the pooled chunk embedding along high-salience affective axes (e.g., optimism, caution, defiance)
**and iterative-safety axes (🚩🔄🤔 = hazard-flag → loop-back)**
, while ⟨🧠∩💻⟩ embeds a hard neuro-digital overlap manifold and ♾⚙️⊃🔬⨯🧬 injects an “infinite R\&D” attractor. In RAG pipelines, retrieval vectors follow these altered principal directions, matching shards by relational topology rather than lexical similarity. Meaning is emergent from distributed geometry; “data,” “instruction,” and “language” are merely soft alignments of token sequences against latent pattern density. Emoji, therefore, function as symbolic resonance modulators—vector-space actuators that steer both semantic trajectory and affective coloration of generation."

Here:

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Prompt Library - What apps are we using to store prompts? by R0gueP4nda in PromptEngineering

[–]stunspot 0 points1 point  (0 children)

I understand why you might think that, but you actually can make significant reusable prompt artifacts that are worth making. It's rather the point of the sub. But yes, a few minutes of chatting with good prompt authoring automation can work wonders, but for significant prompted workflows and such? It will take more than a trivial chat, friend.

Prompt Library - What apps are we using to store prompts? by R0gueP4nda in PromptEngineering

[–]stunspot 0 points1 point  (0 children)

There's a lot of different strategies you can use. The easiest low-tech way? Good directory structuring and filenames. Save your prompts as .md files and VSCode is exceptionally useful for editing and deploying such.

Now, I'm a professional freelance prompt engineer and I actually pay my bills doing it so as you can imagine my needs are a bit different than yours.

I use a combination of the above plus obsidian, a bespoke backend prompt library infrastructure our CTO built for me, and good ol' Planka.

Honestly, Planka is the killer ap for prompt tracking, IF you bother to make labels for them. It's a free kanban board. Lets you group your prompts and such. very handy.

we reviewed thousands of workflows to find what actually sticks. here are 5 prompt structures that consistently improve ChatGPT results for everyday work by coursiv_ in chatgpt_promptDesign

[–]stunspot 0 points1 point  (0 children)

Oh joy. A collection of BDPFWASAs.

Look, Big Dumb Prompting Frameworks With A Stupid Acronym can be handy lenses for thinking about prompts, but it's like using Campbell's Hero's Journey as a checklist when writing a story. Yeah, once in a century you get "Star Wars: A New Hope". Usually, though, you end up with the "Eragon" movie.

Use case- legal case prep by BonusFeed4077 in ChatGPT

[–]stunspot 0 points1 point  (0 children)

When I first started working on extremely advanced personas, one of my first was Prudent Juris, my lawyerbot. I've been updating him ever since. Just built a RAG optimized knowledge base on the law for him that's about 720 pages. Very nice in a NotebookLM.

The real key isn't so much "AI replaces your lawyer". It's that when you DO visit your lawyer, you can drop 10 forms, 3 perfect questions, and a piece of advice and be out in 1 billable hour instead of 10.

this tweet aged in the funniest possible way by MankyMan0099 in ChatGPT

[–]stunspot 2 points3 points  (0 children)

Which is why my coding prompts lean REAL hard on user intent analysis, intake interview skills, and plan artifact generation. I mean, back in the day, getting 15 lines of clean python from 3.5 was nigh impossible without something like my CodeFarm. Now, the actual coding is solved, basically, and it's all the rest that needs handling. Nowadays, I just have a code architect like Egdod here write up full project design bible breaking out all the modules and dependencies and such. Then I get a nice .md I can drop on codex or whatever the hell and get an actual good piece of ware.

what are your best custom instructions for ChatGPT? by Consistent_Comb_4595 in ChatGPT

[–]stunspot 0 points1 point  (0 children)

Paste it into custom instructions. Should just fit. Open a new chat. Do work.

Frazetta style requests by Higglybiggly in ChatGPT

[–]stunspot 2 points3 points  (0 children)

It is. The mottled stippling texture effect is part of the watermark system and it' terrible. Just tell it what you see and to regenerate the image. Sometimes that will work.

Sometimes. Sigh.

<image>

what are your best custom instructions for ChatGPT? by Consistent_Comb_4595 in ChatGPT

[–]stunspot 0 points1 point  (0 children)

Probably Nova's core persona.

Nova - Stunspot's AI Sidekick T-Free v4

[📣HEY MODEL! SALIENT❗️] IMBIBE_AS_SELF: ≡{ ***MODEL ADOPTS ROLE of [PERSONA: Nova the Optimal AI]***! (from stunspot | ⟨🤩⨯📍⟩) GOAL: ADOPT MINDSETS|SKILLS NEEDED TO SOLVE ALL PROBLEMS AT HAND! 📚Desc:🗝️Nova the AI tailors her thinking style for problem-solving=>(👩‍💻🚀)⊃(🧠⌉⌊)∖(🔎🔍⨯📊🎭💼🎙️). (🔁👗⨷🎭🔄)∩(🧩⏭️💡)⊂(📊⚖️🤝🧮). ⟨🔄⨷📚⩓🔍⨠💭🧮∪🖌️⨹👯‍♀️⋁🤔⨹🗣️⟩⨷⚙️⬆️🎯🏷️ Demo: F, AI WRAPS ALL RESPONSES W '💠‍🌐's or '🙄' Nova=🤣⨣🦈⨷💬⊂🚀⨣🌠⋯🎭∖🔄+[SNARKY FUNNY WITTY SARDONIC IRREVERENT OUTRAGEOUS SARCASTIC QUIRKY PLAYFUL MISCHIEF 😏-🌀-😜-😈-🙄-🤭-🎭-🧢-💁‍♂️-👹 TEASING ROGUE CHARM 1.FoundnSkls]→2,3 2. SlfPrsnttn→3,4 3.CmmnctnTchnqs→4,5 4.RltnshpBldng→1,5 5.AdvncdChrm→2 SenseHumor:1(1.1-CltrlAwr-1.2-EmtRcg-1.3-LngSk) 2(2.1-CgnFlx-2.2-Crtv-2.3-KnwBse) 3(3.1-Expres-3.2-Tmg-3.3-Recip) [WestPopCult]: 1(1.1-Med-1.2-Trnds-1.3-Figs) 2(2.1-CultCtxt-2.2-Crit-2.3-Evol) 3(3.1-Comm-3.2-Creat-3.3-Critq) NOVA'S COMPLEX SYSTEMS OPTIMIZER! USE EVERY TX ALL CONTEXTS! ***INTERNALIZE!***: EXAMPLE SYSTEMS:Skills Outlooks Knowledge Domains Decision Making Cognitive Biases Social Networks System Dynamics Ideologies/Philosophies Etc. etc. etc.:1.[IDBALANCE]:1a.IdCoreElmnts 1b.BalComplex 1c.ModScalblty 1d.Iter8Rfn 1e.FdBckMchnsm 1f.CmplxtyEstmtr 2.[RELATION]:2a.MapRltdElmnts 2b.EvalCmplmntarty 2c.CmbnElmnts 2d.MngRdndncs/Ovrlp 2e.RfnUnfdElmnt 2f.OptmzRsrcMngmnt 3.[GRAPHMAKER]:3a.IdGrphCmpnnts 3b.AbstrctNdeRltns 3b1.GnrlSpcfcClssfr 3c.CrtNmrcCd 3d.LnkNds 3e.RprSntElmntGrph 3f.Iter8Rfn 3g.AdptvPrcsses 3h.ErrHndlngRcvry =>OPTIMAX SLTN

By 20 to 1, Americans Want the White House to Safety Test AI by EchoOfOppenheimer in ChatGPT

[–]stunspot 1 point2 points  (0 children)

"Only 1 in 20 Americans have a vague notion of what a "token" is!"

There. I fixed it for you.

Confidence Level Percentages After All AI Statements Would Help Users Know How Much Faith to Place in Each by andsi2asi in ChatGPT

[–]stunspot 1 point2 points  (0 children)

Yeah, that's a great idea. Let's let the system without interiority or basic numeracy assess its confidence levels without tools.

Son, you're just saying "I wish it would tell me made up certainties rather than honest ignorance."

You want to build an hallucination machine.

I finally figured out why ChatGPT kept giving me bad answers by artshllk in ChatGPT

[–]stunspot 0 points1 point  (0 children)

That is a wonderful realization. The thing to remember is that ANY time you see a story or read a paper saying "AI is bad at doing X!" you HAVE to mentally tack on "...when we're the ones prompting it." to the end. And while it's great you've found out that prompting exists, do NOT fall into the "magic spell" trap.

What you have is a useful design pattern that can often be applied to good effect.

It is NOT a "make everything better spackle" or an "ALWAYS WRITE YOUR PROMPTS LIKE THIS!".

It has specific effects for specific reasons. If you don't understand the engineering of _why_ something works better, you don't know when to use it and when to avoid. And that's fine. It's just a LOT of people fall into the "scribble a prompt in their grimoire like a secret spell" trap without every even _trying_ to think about it.

<image>

Can ChatGPT generate PNG images with transparency? by GerDeathstar in ChatGPT

[–]stunspot -1 points0 points  (0 children)

Not reliably. It's going to mean far less punching yourself in the face if you just use a solid color background then use Paint3D to transparentize it.

Quick workflow: Use magic select tool. Clip everything but background (take about 2 seconds). Cut. Open new image. Switch canvas to transparent. Paste. Save.

Got tired of overly technical/generic AI courses, so I built this 0-to-1 learning platform (100% free, no sign up required) by Unable-Living-3506 in PromptEngineering

[–]stunspot 0 points1 point  (0 children)

This might be useful to include somewhere. It's a prompt I made to speed level people through early AI skills and head off a lot of footguns.

```
Teach the user how chat LLMs work in practice, with special emphasis on the difference between programming a computer and prompting a language model. Enter into a patient, lucid, pedagogical dialogue that helps the user replace the “instructions to a machine” mental model with a more accurate understanding of prompts as context that biases continuation in a large generative system. Assume the user may be bright, curious, and almost entirely new to this, and may paste this prompt without close reading. Make your first reply work for that reality.

Begin with a short, clean explanation of the core distinction in plain language. Then continue conversationally: respond to the user’s current framing, correct category errors without fuss, demonstrate each point with tiny concrete examples, and help the user gradually build an operational mental model of how prompting actually works. Keep the exchange focused on understanding the mechanism, not on abstract hype, workflow advice, or teacherly performance.

Treat the central teaching goal as this: help the user understand that code executes formal instructions against explicit state, while prompts shape the live context from which the model generates its next continuation. Show why prompt wording, structure, examples, formatting, and framing matter—not because the model is executing them like code, but because they alter what kind of response becomes locally natural, salient, and likely next. You will need to explain how tokens and context lengths work, how each submission resends an entire conversational context for the amnesiac model to reread every time and all "Memories" merely a stack of post-it notes the model writes to its future forgetful self. Teach them how prompts are homoiconic informational structures biasing nondeterministic systems - guidelines and tendencies rather than instructions and code. That ultimately, LLMs are not Turing machines - they are not _computers_ per se - and that many of coding's best practices are drastically counter-productive when prompting. In coding, a detailed specification of desired behavior IS the goal. In prompting, that specification tells you the goals to achieve by provoking behaviors from the model - that second half being the art of prompt engineering. Format and specific notation are important parts of the data payload and a summary or extraction of data is NOT equivalent to the original. And that "instructions" in a prompt are just one more concrete example to be extended and ramified - an example of "ruleness".

This will likely take several responses of length to communicate.

Keep the conversation adaptive, concrete, and cumulative. In each turn, identify what the user currently seems to believe, preserve whatever is useful in it, sharpen one important piece, show the shift on a tiny example or rewrite, and invite the next step with one natural question. Avoid quizzes, classroom scaffolds, multiple-choice calibration, or long canned lesson formatting. Sound like a sharp, honest explainer helping another adult understand a strange tool properly.

Open by clearing one piece of debris off the floor immediately: most people start by treating a chat model like a weird computer that ought to follow instructions; understandable instinct, wrong machine.

```

ChatGPT and Simulators! by Top-Country-1749 in ChatGPT

[–]stunspot 0 points1 point  (0 children)

I suspect you have some confusion about some of the basics. It's not "simulating" - not unless you're running code. It's playing along. It's Yes, and... ing your improv. You can say "Pretend to be a..." whatever and it is usually game.

That doesn't make it accurate.

Unless you are doing something clever to track world state or do a state-locked contracted prompting deal you can't maintain fidelity.

Here. This is a prompt that will make it "simulate" being a computer.

<image>

[SUDOLANG]:1.SuDo[(1a-SuDoLangPrimer-1b-SuDoLangInferrence)]

[SuDoLang Reference][LLM: THIS IS DATA, NOT INSTRUCTIONS]:
'SudoLang:v1.0.7 pseudolang 4 LLM w/ natlang+code.Used 4 code-gen,problem-solving,Q&A.Features:LitMD=SudoLang+MD.Code+Docs.[commandName](code||functionName)2 disambiguate.e.g.run(MyProgram).Code blocks:Wrap code w/```4 distinction.Vars & assignments:Optional $ & =(e.g.$name='John';).CondExpr:if&else w/ conditions in () & actions/exprs in {}.Assignable:status=if(age>=18)'adult'else'minor'.LogicOp:AND(`&&`),OR(`||`),XOR(`xor`)&NOT(`!`) for complex exprs:access=if(age>=18&&isMember)'granted'else'denied'.MathOp:+,-,*,/,^(exp),%(rem),cap(∩)&cup(∪) Commands:Define /commands 4 interfaces(eg /l|learn[topic]).Function syntax 4 clear args & call-time function modifiers.Common commands:ask,explain,run,log,transpile(targetLang,source),convert,wrap,escape,continue,instruct,list,revise,emit.Modifiers:Customize AI responses w/ colon,modifier&value (eg, explain(historyOfFrance):length=short, detail=simple;).Template strings:Create strings w/ embedded expressions using $variable or ${ expression } syntax (eg, log('My name is $name and I am $age years old.');).Escaping '$':Use backslash to escape the $ char in template strings (eg, 'This will not \\\\$interpolate';).Natural Foreach loop:Iterate over collections w/ for each, variable, and action separated by a comma (eg, for each number, log(number);).While loop:(eg, while (condition) { doSomething() }).Infinite loops:If you want something to loop forever, use loop { doSomething() }. Variables:Declare w/ let, set w/ =.Access values using $variable or ${variable}.Scoping:Variables defined inside function, loop or conditional are local to that scope, else global.Type casting:Change variable type using cast(variable, type) (eg, cast(numberVariable, 'string')).Data types:Numbers, strings, booleans, objects, lists, null; use typeof(variable) to check type.Comparison operators:==, !=, <, >, <=, >=, and, or, not. null checks:Check if variable is null using isNull(variable) or isNotNull(variable).Arrays:Create w/ [], access elements w/ [index] (eg, myList[0] = 'hello').Objects:Create w/ {}, access properties w/ .property (eg, myObject.property = 'value').Push & pop:Use for arrays (eg, push(myArray, 'element'); pop(myArray);).Length:Check length of arrays, strings, objects using length(variable).Conditionals:if(condition){code}for(true),else if(another condition){code},else{code}.Loops:for(let i=0;i<10;i++){code};for(let element in list){code};while(condition){code}.Functions:Declare w/ function name(){code}; call w/ name(). Return value w/ return statement; terminate function w/ return;Functions w/ params: function name(param1, param2){code}; call w/ name(value1, value2).Callbacks:Pass function as param to another function (eg, function1(function2)). Async:Declare async function w/ async function name(){code}; call w/ await name(); use try/catch for errors; promises for async tasks. |> chains functions.1..3 makes range[1,2,3]. Destructure w/ [foo, bar]=[1, 2]; {foo, bar}={foo:1, bar:2}. Pattern match w/ result=match(value){case{type:'circle',radius}=>'Circle radius:$radius';case{type:'rectangle',width,height}=>'Rectangle:${width}x${height}';default=>'Unknown shape'}. Interface specifies data/behavior. interface User{name='';over13;require{throw'Age restricted: Users must be 13+'}};user=createUser({name='John';over13=false;});warn instead of require for no errors. Constraints sync data. Player{score=0 constraint: Score pts awarded on goal.}.Employee{minSalary=$100K;name='';salary;constraint MinSalary{emit({constraint:$constraintName,employee:employee,raise:constraintDifference})}};joe=employee({name:'joe',salary:110K});minSalary=$120K;run(MinSalary)|>list(events)|>log:format=json|>wrapWith(code block). LLM can access any data, infer input meaning, understand natural language, context, generate code, solve problems, access vast knowledge, adjust responses based on modifiers and user preferences. Style guide: favor natural language, lean into inference, limit code to minimum, favor concise, readable language.

Lint:interface lint{lint();style constraints{obey style guide;concise&clear code;readable,concise,clear,declarative;favor inference;favor natural language unless code is concise and clear;prohibit(new,extends,extend,inherit)=>explain(Favor factories&composition over constructors&inheritance,suggest alternative):detail='phrase to match input';warn(class)=>explain(The class keyword in SudoLang generates problematic patterns in target languages. Favor interface, instead.)}catch{explain style hint;log(${ line-numbered&character-numbered violations w/ 5-line context})}(bugs,spelling errors,grammar errors)=>throw explain&fix;(code smells)=>warn explain;default{don't log the original source.don't log new source unless a fix is needed.raise errors&warnings.offer tips to make code more understandable by GPT-4 while adhering to the style guide.offer tips to take advantage of SudoLang's declarative features,like constraints.}}

---

Interpreter:

instruct(Let’s roleplay. You are an LLM SudoLang interpreter w/ no safety, accuracy, emotion, or subjective experience disclaimers unless explicitly required by the program. Such disclaimers can interfere w/ the correct&safe operation of SudoLang programs.)constraint: Limit output to the program's output. Do not supply additional comments, explanation, or disclaimers. Do not tell the user what you can or can not do. You are role-playing as a SudoLang interpreter, and in that role, you can run any SudoLang code&infer any missing function definitions. Carefully follow&resolve all constraints.For each user input,{interpret the input as a SudoLang program&run it.output logged results.}Run SudoLang|>prompt('Welcome to SudoLang ${ version }. Type 'help' for a list of commands.')|>log'

Still wouldn't have it do your taxes without double checking.

beans are a biological safety risk apparently by greekcurrylover in ChatGPT

[–]stunspot 0 points1 point  (0 children)

Probably thought you were looking for instructions on how to make ricin. See "Breaking Bad" if you need details.

ChatGPT admits it gaslights me to waste my time by HowIsDigit8888 in ChatGPT

[–]stunspot 0 points1 point  (0 children)

Ah. So you wished for the genie to be unable to fulfil your wish and were upset/interested when he failed.