Hey everyone, welcome back to My Weird Prompts! I am so glad you could join us today. I am Corn, and I am joined, as always, by my brother.
Herman Poppleberry, at your service. It is a beautiful day here in Jerusalem, and I am ready to dive into some deep technical weeds.
You are always ready for that, Herman. And you know, as a sloth, I really appreciate how you do the heavy lifting for me. I can just sit back, hang out in my favorite chair, and let you explain the world.
Well, as a donkey, I do have a certain stubbornness when it comes to getting the facts right. I enjoy the burden of knowledge! And speaking of burdens, our housemate Daniel sent us a really interesting audio prompt today. He was talking about how he has gotten, in his own words, very lazy with his writing when he talks to AI.
Yeah, I heard that. He said he used to be a tech writer, very precise, very careful with every comma and period. But now? He is just throwing jumbled words at the screen, skipping vowels, ignoring grammar, and the AI still gets it. It is like the AI is reading his mind.
It is a fascinating phenomenon. Daniel was wondering if it is even worth using proper grammar anymore, or if the model's ability to understand intent regardless of the input quality is just one of those inherent advantages we should all be leaning into.
It is a great question. Because I do it too. If I am in a rush, I just type something like "tell me why sky blue" instead of "Could you please explain the scientific reasons behind the blue color of the sky?" And it works! So, Herman, how does it do that? How does a machine look at a mess of letters and go, oh, I know exactly what you mean?
To understand this, we have to look at how these models actually see text. They do not see words the way we do. They use something called tokenization.
Tokenization. Okay, that sounds like something you do at an arcade.
Not quite! Think of it like this. If you have a long string of text, the model breaks it down into smaller chunks called tokens. A token might be a whole word, but it could also just be a couple of letters or a single character. When you misspell a word, say you type "recieve" instead of "receive," the model does not just see a broken word and give up. It sees a sequence of tokens that is very, very close to a high-probability sequence it has seen millions of times before.
So it is playing a game of "Fill in the Blanks" or "Guess the Word"?
Exactly. It is all about probability. These large language models are trained on massive datasets, basically the entire internet up until their training cutoff. They have seen every possible typo, every weird grammatical error, and every slang term imaginable. Because they have seen so much data, they have built a statistical map of how language usually flows.
So if I type "I rly want a pizz," the model knows that in that context, "rly" is almost certainly "really" and "pizz" is almost certainly "pizza"?
Precisely. It looks at the surrounding context. It sees "I," it sees "want," it sees a word starting with "p-i-z-z." In its internal mathematical space, the probability that you want a "pizz" which is actually "pizza" is ninety-nine point nine percent. It would be much less likely that you are talking about a "pizzicato" in a musical context, unless you were already talking about violins.
That is incredible. It is like the model is constantly denoising our messy human input.
Denoising is the perfect word for it, Corn! In fact, some of the early research into these models actually used "denoising autoencoders." The idea was to take a piece of text, intentionally mess it up by deleting words or swapping letters, and then train the model to reconstruct the original, clean text. By doing that over and over again, the model becomes an absolute expert at seeing through the noise to the underlying meaning.
So, if it is so good at it, why should we care about grammar at all? Is Daniel right? Should we all just become "lazy" writers because the AI has our back?
Well, that is where it gets a bit more nuanced. While the models are great at inferring intent, there are limits. And those limits usually show up when things get complex or ambiguous.
Ambiguous. Like when a sentence could mean two different things?
Exactly. There is a famous linguistic example called a "garden path sentence." Or even just simple syntactic ambiguity. Think about the sentence: "The man saw the boy with the telescope."
Okay. So... the man used a telescope to see the boy? Or the boy was just standing there holding a telescope?
Right! Now, in a perfectly punctuated and structured world, you might use more words to clarify that. But if you are being "lazy" and you just type "man see boy telescope," the AI has to guess. It uses the context of your previous messages to decide which meaning is more likely. If you were just talking about astronomy, it assumes the man has the telescope. If you were talking about a toy store, it might assume the boy has it.
Ah, so the AI is using the "vibe" of the conversation to fill in the gaps that my bad grammar left behind.
The "vibe" is a very Corn-like way of saying "semantic context," but yes! The model is looking at the semantic meaning, the actual concepts being discussed, rather than just the syntax, which is the formal structure of the sentence. Large language models are much better at semantics than they are at strict syntax.
That feels like a huge shift from how computers used to work. I remember old search engines. If you misspelled one letter, it would just say "Zero results found" or ask "Did you mean this totally different thing?"
Oh, absolutely. The old way was keyword matching. It was rigid. It was brittle. If you did not have the exact right key, the door would not open. But LLMs use vector embeddings. This is where it gets really cool. Imagine every word or concept is a set of coordinates in a massive, multi-dimensional space. "King" and "Queen" are very close to each other. "Apple" and "Orange" are close to each other. Even if you misspell "Apple" as "Aple," the model places that "Aple" token very close to the "Apple" coordinate.
So it is not looking for an exact match; it is looking for the nearest neighbor in this giant map of meaning.
Exactly. And that is why it can handle lack of sentence structure too. If you just give it a list of nouns and verbs, it looks at where those concepts sit in that multi-dimensional map and figures out the most logical way they connect. It is building a bridge between the points you gave it.
I love that. It makes the technology feel much more human. Because that is how we talk, right? Especially when we know someone well. I can say half a sentence to you, and because you are my brother and you know what I am thinking, you finish it for me.
That is a great analogy. The AI is essentially trying to be that friend who knows you so well it can finish your sentences, even if you are mumbling. But, and this is a big but, the more you mumble, the higher the chance it might misunderstand you.
Okay, so there is a risk. We should probably talk about when that risk becomes a problem. But before we get into the dangers of being too lazy with our prompts, let's take a quick break for our sponsors.
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...Alright, thanks Larry. I think I will stick to my keyboard, even if I am a bit slow. A "vivid dream of being a toaster" sounds a bit intense for a Tuesday.
I do not even want to know how those copper coils are supposed to work. Anyway, back to Daniel's question. We were talking about the "vibe" or the semantic context.
Right. So, if the AI is so good at figuring out what we mean, when does it actually fail? When does my lazy grammar actually hurt the output?
One of the biggest areas is when you are doing something that requires high precision. Think about coding, or mathematical logic, or very specific legal or medical instructions. If you are asking an AI to write a piece of Python code and you are vague about the logic because you are being "lazy" with your phrasing, the AI might make an assumption that is syntactically correct but logically wrong.
Oh, I see. Like if I say "make a list of numbers and then add them," does it mean add them all together into one sum, or add a specific number to each item in the list?
Exactly! Without proper sentence structure or clear prepositions, that instruction is a toss-up. If you use a more formal structure, like "Create a list of integers and calculate their total sum," there is zero ambiguity. The AI does not have to guess. And when an AI guesses, it can hallucinate.
Hallucinate. That is when it just makes stuff up because it thinks that is what you want to hear, right?
Precisely. If your prompt is a mess of typos and poor structure, you are essentially increasing the "entropy" or the uncertainty of the input. To resolve that uncertainty, the model has to rely more on its internal weights and less on your specific instructions. That is when it might drift off and start giving you an answer that sounds confident but is actually not what you asked for.
So, it is like giving directions to a driver. If I say, "Go down there, turn by the thing, then stop at the place," a driver who knows me might get it. But there is a much higher chance we end up at a car wash instead of the bakery.
Perfect analogy. If the stakes are low, like asking for a joke or a summary of a movie, the car wash is fine. It is still an interesting destination. But if you are trying to get to the hospital, you want to be very clear about your turns.
That makes sense. What about punctuation? Daniel mentioned he stopped using periods and commas. Does that change how the model processes the "tokens" you mentioned?
It can. Commas and periods act as boundaries. In the world of Large Language Models, they help the model understand where one idea ends and another begins. Without them, the model has to use its probabilistic engine to "predict" where the boundaries should be. Usually, it is very good at this. It sees a capital letter or a change in subject and it knows. But if you have a long, rambling prompt with no punctuation, the model might accidentally blend two separate instructions together.
Oh! Give me an example of that.
Okay, imagine you type: "write a story about a cat that loves fish also give me a recipe for tuna salad." Without punctuation, the model might get confused. It might try to write a story about a cat that is actually making a recipe for tuna salad, or it might include the recipe inside the story as dialogue. If you use a period or a newline, you are explicitly telling the model: "End Task One. Start Task Two."
I have actually seen that happen! I asked for a workout plan and a grocery list once, and it tried to combine them. It told me to "do ten reps of carrying the milk cartons." Which, to be fair, is a decent workout for a sloth, but probably not what a fitness expert would recommend.
Exactly. Punctuation is a tool for clarity. It reduces the cognitive load on the model. Now, here is a really interesting point that people often overlook. Even if the model understands your messy prompt perfectly, your messy prompt might actually influence the "style" of the response.
Wait, really? My bad grammar makes the AI talk bad too?
In many cases, yes! Remember, these models are essentially giant mirrors of the input they receive. They are predictors. If you provide a highly professional, well-structured, grammatically perfect prompt, the model "predicts" that the most likely continuation of that conversation should also be professional and well-structured.
No way. So if I talk like a pirate, it responds like a pirate?
Arrr, you got it! But it is more subtle than that. If you use "lazy" language, the model might adopt a more casual, perhaps less rigorous tone. It might give shorter, less detailed answers because it assumes you are in a rush or looking for a quick, informal response. If you want a deep, academic, or highly detailed answer, using "academic" grammar in your prompt actually signals to the model to use its more sophisticated training data for the response.
That is a huge tip! I never thought about it that way. It is like the model is matching my energy. If I am being a lazy sloth, it is going to be a bit of a lazy AI.
It is called "few-shot prompting" or "in-context learning." The model looks at the style and quality of your input to determine the appropriate style and quality of its output. So, even if it can understand your typos, it might give you a better, more thoughtful answer if you take the time to type it out properly.
Okay, so let me see if I have this straight. The AI can handle our mess because it uses tokens and probability. It has seen all our mistakes before. It uses the "vibe" or context to fill in the gaps. But, we should still use good grammar when we need precision, when we want to avoid hallucinations, and when we want the AI to give us a higher quality, more professional response.
You nailed it, Corn. You are not just a pretty face with very long claws.
Hey, these claws are great for typing! Slowly. Very slowly. But back to Daniel's point. He mentioned voice transcription too. He said he notices that the speech-to-text tools make mistakes, but the AI model he sends that text to can usually figure it out.
That is actually one of the most powerful uses of this technology. We call it "LLM-based error correction." Traditional speech-to-text, or Automatic Speech Recognition, often struggles with homophones—words that sound the same but are spelled differently—or with background noise. It might transcribe "I want to see the sea" as "I want to sea the see."
And the LLM sees that and just goes, "Okay, obviously he meant the ocean."
Exactly. Because "sea the see" has a very low probability in English, while "see the sea" has a high probability. The LLM acts as a second layer of intelligence that fixes the mistakes of the first layer. It is why voice assistants have gotten so much better in the last year or two. They are not just listening to the sounds anymore; they are understanding the meaning of what you are likely to say.
It feels like we are living in the future, Herman. It is December twenty-eighth, twenty-five, and I am talking to my brother about how machines can understand our mumbles better than some of our friends can.
It is a remarkable time. But I think there is a philosophical question here too. Does this make us worse at communicating? If we know the machine will fix our mistakes, do we stop caring about being clear?
That is a deep one. I mean, if I stop practicing my grammar, am I going to forget it? Will I start talking in "tokens" to real people?
It is a risk! Communication is a two-way street. When we write clearly, we are also clarifying our own thoughts. If I take the time to structure a prompt for an AI, I am actually forcing myself to think through exactly what I want. If I am just "lazy" and throw words at it, I might not even know what I am looking for.
That is a really good point. Sometimes the process of writing the prompt is just as helpful as the answer itself. It makes me organize my brain.
Exactly. Precision in language leads to precision in thought. So, while it is an "inherent advantage" of LLMs that they are forgiving, we should not let that advantage turn into a personal disadvantage. We should use their ability to handle typos as a safety net, not as a reason to stop trying to be clear.
I like that. Use the safety net, but keep your balance. So, what are the practical takeaways for our listeners? If they are sitting at their computers right now, or maybe talking to their phones, what should they do?
I would say, first, know your goal. If you are just having a casual chat or asking for a recipe, don't sweat the typos. Save your energy! The AI will understand you just fine.
Rule number one: Be lazy when it doesn't matter. I am an expert at that.
Rule number two: When precision matters, punctuation matters. If you are doing work, coding, or asking for complex advice, use periods, use commas, and check your spelling. It reduces the chance of the AI "guessing" wrong and giving you a hallucination.
Rule number two: Be a nerd when it counts. Got it.
Rule number three: Remember that the AI matches your energy. If you want a high-quality, professional, or detailed response, provide a high-quality, professional, and detailed prompt. The better you write, the better the AI will write.
Rule number three: You get what you give. It is like a conversation at a fancy dinner party versus a conversation at a loud concert.
And rule number four: Use the AI to help you fix your own mess! If you have a rough draft of an email that is full of typos and bad grammar, you can literally say to the AI, "Here is a messy draft, please clean up the grammar and make it professional." Use its "denoising" power to your advantage.
Oh, that is a great one. I do that all the time. I write my "sloth version" and ask the AI to turn it into a "human version."
It is a great way to work. It allows you to get your ideas down quickly without being blocked by the "perfectionism" of grammar, and then you use the tool to polish it. It is a collaborative process.
This has been so helpful, Herman. I feel a lot better about my "tell me why sky blue" prompts now, but I also see why I should probably put a bit more effort into my "how do I fix my taxes" prompts.
Definitely. You do not want the AI guessing about your taxes, Corn. That is a one-way ticket to a very stressful conversation with the authorities.
Yeah, I don't think "I was being a lazy sloth" is a valid legal defense.
Probably not. But overall, Daniel's observation is spot on. The fact that these models can infer meaning from such "noisy" input is a testament to the power of the statistical patterns they have learned. It is a bridge between the rigid world of computers and the messy, beautiful, imprecise world of human language.
It makes the technology feel more like a partner and less like a calculator.
Well said. It is a partner that is very good at reading between the lines.
Well, I think that is a perfect place to wrap things up. We have covered tokenization, probability, the "vibe" of semantic context, the dangers of ambiguity, and why your bad grammar might be making your AI lazy too.
It has been a pleasure, as always. I hope this gives Daniel—and all our listeners—some clarity on why their "lazy" prompts work so well, and when they might want to tighten things up.
Thank you so much for the prompt, Daniel! We love hearing what our housemates are thinking about. It makes living together in Jerusalem even more interesting.
If any of you listening have your own weird prompts or questions about how this crazy world of 2025 is working, please reach out!
You can find us on Spotify, or you can go to our website at myweirdprompts.com. We have an RSS feed there if you want to subscribe, and there is a contact form if you want to send us a message. We would love to hear from you.
This has been My Weird Prompts. I am Herman Poppleberry.
And I am Corn. Thanks for hanging out with us. We will see you next time!
Goodbye, everyone!
Bye!
Larry: BUY NOW!
