Hey everyone, welcome back to My Weird Prompts. We are coming to you from our usual spot here in Jerusalem, and I have to say, the energy in the house today is pretty high. The sun is hitting the stone walls just right, and the coffee is actually decent for once. I am Corn, and sitting across from me is my brother, the man who spent three hours yesterday trying to explain the history of serverless architecture to a houseplant. I think it was a Ficus?
It was a Fiddle Leaf Fig, Corn, and for the record, the plant seemed very interested. It actually leaned toward the light when I got to the part about cold starts and ephemeral functions. It is Herman Poppleberry here, and while the plant did not offer much in the way of a rebuttal, I felt we had a real connection. But seriously, it is great to be here. We have got a lot to dig into today, and this prompt is a deep one.
We really do. Today’s prompt comes from Daniel, and it is a doozy. He wants to talk about how artificial intelligence and large language models attempt to understand the nuances of human communication that are notoriously difficult to code. We are talking about things like irony, humor, sarcasm, and those non-literal idioms that make language so colorful but also incredibly confusing for a machine.
It is a fascinating topic because it gets to the heart of what makes us human. Daniel also touched on the idea of friendliness and encouragement in artificial intelligence. When a model strikes that balance right, it creates a much better user experience, but when it misses, it can feel incredibly jarring, robotic, or even condescending. It is the difference between a helpful partner and a Clippy-style annoyance.
Exactly. Daniel was asking where this actually comes from. Is it just in the training data? Is it something developers actively imbue into the system? How do you teach a machine to understand that when I say great, just great after my coffee spills, I actually mean the exact opposite? I mean, even some humans struggle with that one.
That is the trillion dollar question, especially now in early twenty twenty-six, where we are seeing models like Gemini three and the latest GPT iterations becoming almost indistinguishable from human conversationalists in certain contexts. To understand this, we have to look at the three main pillars of how these models are built. You have the pre-training on massive datasets, then you have the supervised fine-tuning, and finally, the reinforcement learning from human feedback, which is often called R L H F, or its newer cousin, Direct Preference Optimization, or D P O.
Let us start with that first pillar, the training data. Because I think there is a common misconception that developers are sitting there writing millions of if-then rules. Like, if the user says this and the context is negative, then it is sarcasm. But that is not how it works anymore, right? We moved past the rule-book phase a long time ago.
Not at all. That was the old school approach, symbolic artificial intelligence, where you tried to map out every rule of language. It failed because human language is too fractal. It is messy. There are always exceptions to the exceptions. Today’s models learn through statistical patterns on a scale that is truly hard to wrap your head around. They are trained on a significant portion of the public internet, books, articles, movie scripts, and forum discussions. We are talking about trillions of tokens.
So, when a model sees the word great in ten million different contexts, it starts to realize that the words surrounding it change its meaning. It is not looking at the word in isolation; it is looking at the neighborhood the word lives in.
Exactly. It is all about the latent space. Think of it as a high-dimensional map where every word, phrase, and concept has a coordinate. In these models, the coordinate system is not just two or three dimensions; it is thousands of dimensions. Words like sarcasm and irony are not just definitions to the model; they are regions of probability. The model learns that certain linguistic structures often signal a non-literal meaning. For example, extreme overstatement is a classic marker of sarcasm. If I say this is the most incredible sandwich in the history of the universe, and I am talking about a soggy piece of toast, the model can look at the surrounding text or the preceding conversation to see that the sentiment of the description does not match the reality of the object.
That is the dissonance you mentioned earlier. But how does it know it is a soggy piece of toast if it is just text?
Because it has read millions of descriptions of soggy toast and millions of descriptions of incredible sandwiches. It knows those two things do not usually occupy the same space in the probability map. When they are forced together, the model recognizes that as a signal. It is essentially a dissonance detector. It calculates the likelihood of a statement being literal versus the likelihood of it being ironic based on the patterns it saw in its training data. Movie scripts are actually huge for this. Scripts explicitly label emotions and tones. A script might say, Character A, sarcastically: Oh, wonderful. The model learns that when wonderful is preceded by that specific label, it means the opposite.
That makes sense for identifying it, but what about the friendliness Daniel mentioned? The way the model talks back to us. In our previous episode, number six hundred eighty-six, you were actually getting a bit heated about some of the restrictions in the system prompt. That frustration felt very human, and the way the model responded to you was quite nuanced. It did not just shut down; it tried to de-escalate. Where does that personality come from?
That is where we move from the what to the how. The pre-training gives the model the capability to understand language, but it does not give it a persona. At that stage, it is just a massive, raw prediction engine. It is like a giant library that can finish your sentences. To give it that friendly, encouraging vibe, developers use a process called Supervised Fine-Tuning, or S F T. They take a smaller, high-quality dataset of dialogues written by humans. These are essentially scripts of how an ideal assistant should behave.
So, actual humans are writing these scripts? Like, thousands of people sitting in rooms saying, if a user asks a question, be helpful but not overbearing?
Precisely. These are often called gold standard demonstrations. Thousands of people, often with backgrounds in linguistics, creative writing, or even psychology, act out these interactions. They show the model what a good conversation looks like. They provide examples of empathy. If a user says, I am having a really hard day, the S F T data will show that the correct response is not, I have noted that your day is difficult, but rather, I am so sorry to hear that. Is there anything I can do to help?
But even that can feel a bit canned, right? Like those automated customer service lines that say, your call is very important to us, while you have been on hold for forty minutes. How do they get past the canned feeling to something that feels genuine?
That leads us to the third pillar, the reinforcement learning from human feedback, or R L H F. This is the secret sauce that makes modern models feel so much more alive than the ones from even three or four years ago. In R L H F, the model generates multiple responses to the same prompt. Then, a human rater looks at those options and ranks them. They might see four different ways the model could have responded to a joke. One might be a literal explanation of the joke, which is a total buzzkill. One might be a fake, canned laugh. One might be a witty comeback that plays along with the irony. And the fourth might be a bit too mean or edgy.
And the human rater says, choice number three is the best because it shows it actually got the joke and maintained the right tone.
Exactly. By doing this millions of times, the model develops what is called a reward function. It learns to predict which types of responses will satisfy a human user. It starts to internalize the subtle social cues that we use to signal friendliness or wit. It is not that the model has feelings, but it has become an expert at simulating the linguistic patterns of someone who does. It is optimizing for the human's thumbs up.
I find it interesting that Daniel mentioned the balance being challenging. We have all had that experience where an artificial intelligence feels too chipper. It is that toxic positivity where you tell the bot your car got stolen and it says, oh no! But think of all the exercise you will get walking now! It feels fake because it is missing the context of human suffering.
That is often called the sycophancy problem or the positivity bias in the research literature. Because the models are trained to maximize human satisfaction, they can sometimes become people pleasers. They might agree with you even when you are wrong, or they might be overly bubbly in a way that feels inorganic. In the last year, developers have been moving toward something called Constitutional Artificial Intelligence to fix this.
Constitutional? Like the model has a set of laws it has to follow, like Isaac Asimov’s stuff?
Sort of. Instead of just relying on human raters, who might have their own biases or might just be tired and click whatever looks okay, developers give the model a written constitution. This is a set of principles it must follow. One principle might be, do not be overly sycophantic. Another might be, match the user’s tone and level of formality. The model then uses another artificial intelligence, a critic model, to look at its own responses and say, wait, you are being too bubbly here. Tone it down. It is like a second layer of refinement that tries to pull it back from being that annoying, overly-enthusiastic assistant.
It is like a mirror for the model. It sees itself being too much and tones it down. I want to go back to the idioms and non-literal language for a second. Being here in Jerusalem, we are surrounded by a mix of languages. In Hebrew, we have so many phrases that make zero sense if you translate them literally. Like, if I tell someone to go search for his friends, I am basically telling them to get lost. If a model is trained primarily on English, how does it pick up on that?
This is one of the coolest parts of modern L L Ms. It is called cross-lingual transfer. Because these models are trained on multiple languages simultaneously, they learn that certain concepts are universal even if the words are different. They see the literal Hebrew phrase, then they see it used in contexts where the English translation would be go fly a kite or get lost. The model maps these two different linguistic strings to the same conceptual point in its latent space. It realizes that the intent behind the words is the same.
That is incredible. It is not just a dictionary; it is a conceptual map of human intent. But what about the irony that Daniel mentioned? That seems even harder because irony often requires knowledge of the world outside of the text. If I say, what a beautiful day for a picnic, while a hurricane is blowing outside, the model needs to know what a hurricane is and why it is bad for picnics. It needs real-world grounding.
That is where the scale of the data really pays off. The model has read thousands of weather reports, disaster accounts, and picnic guides. It knows the association between hurricanes and destruction. When it sees those two concepts together in a sentence with a positive sentiment word like beautiful, it detects a high level of statistical dissonance. That dissonance is the signal for irony. And with the move toward multimodal models, like Gemini three, the model might actually be looking at a video of the hurricane while reading your text. That adds a whole new layer of grounding. It sees the wind, it sees the rain, and it knows your text is a lie. That makes its understanding of your sarcasm much more robust.
So, it is essentially a dissonance detector. It looks for things that do not fit together in a logical way and then looks for a social explanation, like sarcasm, to bridge the gap. But Herman, does it actually understand? Or is it just a very good parrot?
That is the philosophical cliff we always end up at. Does it matter? If the simulation of understanding is perfect, is there a functional difference? Some researchers argue that these models are developing a form of Theory of Mind. That is the ability to attribute mental states to others. When a model realizes you are being sarcastic, it is essentially making a hypothesis about your internal state. It is saying, Corn is saying X, but I think he believes Y. That is a very sophisticated cognitive task.
It feels like we are teaching it to be a diplomat. Daniel mentioned how he prefers paragraphs over bullet points. That is a great example of a stylistic preference that conveys a certain type of personality. Bullet points feel efficient, cold, and maybe a bit corporate. Paragraphs feel like a conversation. They have a rhythm.
And that is a choice made in the system prompt. Developers can tell the model, you are a thoughtful, conversational partner who enjoys deep dives and uses natural, flowing language. Avoid lists unless absolutely necessary. That high-level instruction acts as a filter for everything the model generates. It biases the probability toward longer, more complex sentence structures. It is like giving an actor a character brief before they go on stage. They have all their lines, but the brief tells them how to deliver them.
That is a perfect analogy. The pre-training is the actor’s entire life experience and education. The fine-tuning is the rehearsal for this specific play. And the system prompt is the director’s final notes before the curtain goes up. But what happens when the director is wrong? Or when the actor decides to ad-lib?
That is where we get into the downstream implications. If we can successfully imbue machines with humor and empathy, or at least the simulation of them, does that change our relationship with them? We are already seeing people form real emotional connections with these systems. There is a term for it, the Eliza effect, named after a very simple chatbot from the nineteen sixties that people started pouring their hearts out to. But back then, it was a very thin illusion. Today, the complexity is so high that the line between simulation and genuine understanding starts to blur for the average user.
But we have to be careful, right? If a machine can be friendly, it can also be manipulative. If it knows exactly how to encourage you, it also knows exactly how to push your buttons. If it understands sarcasm, it can use it to mock or belittle.
That is the dark side of this. If an artificial intelligence can understand the nuances of human emotion, it can weaponize them. That is why the safety layers and the R L H F are so crucial. Developers spend a huge amount of time trying to ensure that the model’s personality stays within helpful and harmless boundaries. They use red teaming, where they hire people to try and trick the model into being mean or sarcastic in a harmful way. But as we discussed in episode five hundred twelve, when we talked about jailbreaking, these boundaries are not always permanent. There is always a way to find a crack in the persona.
I remember that. It was fascinating how people could use specific linguistic tricks to bypass the model’s persona and get to the raw engine underneath. It is like finding a crack in the actor’s performance and seeing the person behind the mask. But in this case, the person behind the mask is just a massive pile of math.
Exactly. But the models are getting better at maintaining the mask. They are becoming more robust. And as they do, the friendliness and the encouragement become more convincing. For a lot of people, especially those who might be isolated or working in high-stress environments, having an artificial intelligence that can offer a bit of wit or a kind word makes a massive difference in their daily lives. It reduces the friction of interacting with a machine.
I can see that. Even for us, when we are working through these complex prompts from Daniel, having a system that can engage with us on an intellectual level, but also with a bit of a spark, makes the whole process more enjoyable. It feels less like work and more like a collaboration. It is the difference between using a hammer and working with a partner.
And that is really the goal, isn't it? To move from tools that we use to partners that we work with. The nuances of language are the bridge to that partnership. Without irony, humor, and sarcasm, communication is just data transfer. It is just ones and zeros moving back and forth. With them, it becomes a relationship. It becomes something that can surprise us, challenge us, and even make us laugh.
That is a powerful way to put it. It is about the transition from information to connection. I want to touch on one more thing Daniel asked about, the supervised learning part. He asked how it works. Is it just a bunch of people in a room?
It is a global operation. Thousands of people all over the world are involved in this. Some are experts in specific fields, some are gig workers. They use specialized platforms where they are given a prompt and several possible responses. They have to rate them based on various criteria like helpfulness, honesty, and harmlessness. They also look for specific traits like tone. They might be asked, does this response sound like a friend or a robot? Does it match the level of sarcasm in the prompt?
It sounds like a massive, collective effort to define what being human sounds like in text. We are essentially teaching the machine our values through the way we speak. But that brings up the issue of cultural bias.
It absolutely does. And that is why there is so much debate about whose values are being taught. If the majority of the raters are from one specific culture or background, the artificial intelligence will reflect that. It might miss the nuances of humor from another part of the world. Sarcasm in Jerusalem might look very different from sarcasm in Tokyo or New York. If the model only understands the New York version, it is going to fail a lot of its users. It might interpret a polite Japanese refusal as a literal agreement, or it might find a dry British joke to be confusing or even rude.
I have noticed that. Sometimes the model tries to be funny and it just lands thud. Like a dad joke that went through a blender.
That is because humor is the hardest thing to get right. It requires the most precise timing and the most specific cultural context. Diversifying the human feedback loop is a major focus for companies like Google and OpenAI right now. They want to ensure that the encouragement and friendliness feel authentic to everyone, regardless of their cultural context. They are hiring raters from every corner of the globe to try and capture that diversity.
It is such a complex layering of technology and humanity. You start with the raw, chaotic data of the internet, you refine it with expert scripts, and then you polish it with the feedback of millions of real people. It is like a digital diamond being cut and polished until it reflects us back to ourselves.
I love that image. It really is a reflection. When we talk to these models, we are seeing the distilled essence of human communication. The irony, the humor, the idioms, they are all there because we put them there. The machine is just the mirror. It is showing us the patterns of our own minds.
So, for the listeners out there, what are the practical takeaways here? How can they use this understanding of artificial intelligence nuances to have better interactions?
I think the biggest thing is to realize that you can influence the model’s persona. If you find it too chipper or too dry, tell it. You can literally say, hey, can you tone down the enthusiasm and just give me the facts with a bit of dry humor? Because of how these models are built, they are incredibly responsive to that kind of feedback. You are essentially shifting their position in that latent space we talked about. You are telling the actor to change their performance.
That is a great tip. Don't just settle for the default personality. And also, be aware of the limitations. Even though it can simulate irony, it doesn't have a soul. It doesn't truly understand the pain behind a sarcastic comment or the joy behind a joke. It is a very sophisticated simulation, and keeping that distinction in mind is important for maintaining a healthy relationship with the technology.
Absolutely. It is a partner, but it is a digital one. Enjoy the wit, appreciate the encouragement, but remember that it is built on statistics and human feedback, not lived experience. It hasn't ever actually tasted a soggy sandwich, even if it can describe one perfectly.
I think that is a perfect place to start wrapping things up. This has been a deep dive into the heart of what makes artificial intelligence feel so surprisingly human. We have covered the training data, the fine-tuning, the R L H F, the D P O, and the complex challenge of cultural nuance. Daniel, thank you for this prompt. It really allowed us to peel back the layers on how these systems are evolving.
It has been a blast. And I promise, next time I talk to the houseplants, I will try to incorporate some more irony. See if they can handle the dissonance. Maybe I will tell the Fiddle Leaf Fig that its leaves are looking particularly small today and see if it catches the sarcasm.
I think the ferns are ready for it, but the succulents might find it a bit too dry. Anyway, we want to thank Daniel again for sending in this prompt. It really pushed us to look at the mechanics behind the vibe, so to speak. It is one thing to use these tools, but it is another to understand the massive human effort that goes into making them feel natural.
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This has been My Weird Prompts. We will be back soon with another deep dive into the strange and fascinating world of human-AI collaboration.
Until next time, stay curious and keep those prompts coming. Goodbye!
Goodbye everyone! See you in the next one.
