Hey everyone, welcome back to My Weird Prompts. It is February twenty-sixth, twenty twenty-six, and I am here with my brother, as always, ready to dive into the deep end of the A-I pool.
Herman Poppleberry here, at your service. And Corn, I have to say, today’s prompt from Daniel really gets to the heart of the next big architectural shift we are seeing in the industry. We have spent the last three years obsessing over the size of the model, but now, we are finally starting to obsess over the shape of the data.
It really does. Daniel is asking about the rise of domain-specialized A-I models. He’s looking at this transition from these massive generalist models like Opus four point six or the latest from Google and OpenAI, and wondering if we are heading toward a world of ultra-specialist models. Specifically, he wants to know which approach is going to win out: fine-tuning, integrated retrieval augmented generation, or training small, lean models from scratch on specialized data.
It is a brilliant question because it touches on the efficiency crisis we are currently facing. We have spent the last few years essentially trying to build a brain that knows everything from how to write a haiku to how to debug kernel drivers. But if you are an architect in Jerusalem trying to navigate local building regulations, you do not really need your A-I to know the history of medieval poetry or what people are arguing about on Reddit. In fact, that extra knowledge might actually be getting in the way.
And Daniel actually mentioned that specific example, the building ordinances in Israel. It is such a great use case because it is hyper-specific, it is high-stakes, and it requires a level of precision that generalist models often struggle with because they are trying to balance so much conflicting information from their training sets. If you ask a generalist model about a zoning law, it might accidentally hallucinate a rule from California because it saw ten thousand more documents about San Francisco than it did about Jerusalem.
Right. To set the stage here, we should acknowledge that for a long time, the consensus was that more parameters equal more intelligence. The idea was that the generalist models have this emergent reasoning capability that you only get when you train on the whole internet. But as we move into twenty twenty-six, we are realizing that while those models are great reasoning engines, they are incredibly expensive to run, they have massive latency issues, and they carry a lot of baggage. We are seeing the "Data Wall" hit the industry. We have run out of high-quality public text to scrape, so the only way to get smarter now is to get more specific.
That baggage is the key. When Daniel talks about training models without irrelevant data like Reddit or poetry, he is talking about increasing the data density. If ten percent of your training data is high-quality legal text, the model spends a lot of its capacity learning the structure of law. But if ninety-nine percent of its training data is high-quality legal text, you can probably get the same level of legal expertise in a model that is a fraction of the size. We are talking about the difference between a one-hundred-billion parameter model and a seven-billion parameter model performing at the same level in a specific niche.
Precisely. It is about the signal-to-noise ratio. We touched on this back in episode eight hundred ten when we discussed the agentic interview and how models learn to know you. The same principle applies to domains. If the model’s entire world is architectural blueprints and structural engineering standards, its internal representations of those concepts are going to be much more refined than a generalist model that also has to remember the lyrics to every pop song from the nineteen eighties. Think about the "latent space" of these models. In a generalist, the concept of a "bridge" is connected to music, dental work, and civil engineering. In a specialist model, "bridge" has a much tighter, more technical definition.
So let’s break down the three paths Daniel mentioned. First, we have the current favorite, which is R-A-G, or retrieval augmented generation. This is where you take a generalist model and you give it a library of documents to look at. When you ask a question, it finds the relevant snippet and uses its general reasoning to explain it to you. Herman, why is this the default right now, even in early twenty twenty-six?
It is the default because it is the easiest to implement. You do not have to train anything. You are basically giving the A-I a pair of glasses and a book. It uses its existing brain to read the book. It is great for accuracy because the model can cite its sources. But the downside is latency and context window costs. Even with the massive context windows we have now, as we discussed in episode eight hundred forty-six, just dumping data into a prompt is not a long-term solution for deep expertise. There is also the "lost in the middle" problem. Even the best models today struggle to maintain perfect recall when you shove ten thousand pages of building codes into their context window.
Right, because the model is still a generalist at heart. It might understand the words in the building code, but it does not have that deep, intuitive grasp of the domain that comes from seeing millions of examples during its formative training. It is like a smart person reading a manual for the first time versus a professional who has lived and breathed the subject for twenty years. The professional doesn't just "retrieve" the information; they "know" the information. It is part of their fundamental logic.
That is a perfect way to look at it. And that brings us to the second path: fine-tuning. This is where you take that generalist model and you do a bit of extra training on a specific dataset. You are trying to nudge its weights to be more specialized. In twenty twenty-four and twenty twenty-five, we saw a lot of success with P-E-F-T, or parameter-efficient fine-tuning, like LoRA. But the problem here is what we call catastrophic forgetting. If you push a model too hard to become a legal expert, it might actually lose some of its general reasoning or its ability to follow basic instructions.
I have seen that happen. You end up with a model that speaks like a lawyer but forgets how to format a simple list or follow a negative constraint. It is a delicate balance. And it still does not solve the underlying issue that the model is fundamentally built on a foundation of general internet data. You are trying to paint a house that was built for a different purpose. You can put a "Lawyer" coat of paint on a "Reddit" house, but the foundation is still made of memes and movie reviews.
Which leads us to Daniel’s third option, and the one I am personally most excited about: training small, lean models from scratch as experts. This is the idea of vertical pre-training. Instead of a trillion-parameter generalist, you train a ten-billion or even a one-billion parameter model on a highly curated, domain-specific corpus. This was once considered too expensive, but with the advancements in synthetic data and better training algorithms we have seen over the last year, it is becoming the gold standard for high-stakes industries.
This is where the token economics get really interesting. If you are training a model from scratch, you can be incredibly picky about what it eats. If you want an A-I for Israeli architecture, you give it every building permit ever filed in Jerusalem, every zoning law, every structural engineering paper, and maybe some high-quality physics simulations. You leave out the celebrity gossip and the movie reviews. You are essentially creating a "digital savant."
And what you get is a model that is incredibly efficient. Because it does not have to dedicate any of its neural pathways to understanding things that are irrelevant to its job, it can achieve a level of depth that rivals much larger models. We are seeing this with some of the specialized medical models on Hugging Face right now. They are tiny compared to the big frontier models, but in their specific niche, they are more accurate and much faster. They don't just know the facts; they understand the underlying causal relationships of the domain.
It makes me think about the hardware implications too. We talked about this in episode six hundred thirty-three, the memory wars. If you can run a highly capable expert model on local hardware, like a laptop or even a phone, that changes the game for privacy and accessibility. An architect on a construction site in the middle of a city doesn’t want to wait for a massive model in the cloud to process a request over a patchy connection. They want an expert in their pocket. They want a model that can run on an N-P-U, a neural processing unit, without needing a massive server farm.
That is the dream of the mobile A-I agent, which we covered in episode four hundred seventy-seven. But Daniel’s prompt adds another layer: the fleet of models. He’s talking about agentic A-I where you have a group of these small experts working together. Imagine you have a lead planner model—the "Foreman"—and it realizes it needs to check a specific building code. It doesn’t try to do it itself; it delegates that task to the specialist model that was trained specifically on that code. Then it asks the structural engineering model to verify the load-bearing requirements.
That is the sub-agent delegation we explored in episode seven hundred ninety-five. It feels like the natural evolution of the technology. Instead of one giant, lumbering god-model, we have a sleek, coordinated team. It is like the difference between one person trying to build a whole house alone and a crew of specialized contractors. You have the electrician, the plumber, the carpenter. They each know their domain deeply, and they work together under a foreman. This modularity is key for twenty twenty-six.
That is one of our two analogies for the day, folks! But it is a good one. The foreman is the generalist or the planner model, and the contractors are these lean, specialist models Daniel is talking about. This approach solves so many problems. It solves the latency issue because you are only calling the specialist when you need it. It solves the accuracy issue because that specialist has been trained on the ground truth of that domain. And it solves the cost issue because you aren't using a massive amount of compute for a task that only requires a small amount of specialized knowledge. Why use a sledgehammer to hang a picture frame?
So, looking at Daniel’s question about which approach will be most effective, I think the answer is actually a hybrid, but leaning heavily toward those small, specialized models. R-A-G will always be part of the mix because you need to be able to look up real-time data or specific documents that weren't in the training set. Even the best architect needs to look at the specific blueprints for the project they are working on today.
Right. You can’t train a model on a blueprint that was drawn yesterday. So R-A-G is the short-term memory. But the deep expertise, the fundamental understanding of the rules and the logic of the domain, that should be baked into the weights of a specialized model. I think we are going to see a massive shift toward what I call vertical pre-training. We are moving from the era of "Big A-I" to the era of "Deep A-I."
Vertical pre-training. Explain that a bit more for our listeners. How does it differ from what we saw in the early days of LLMs?
So, traditional pre-training is horizontal. You are trying to cover the entire width of human knowledge. You are grabbing everything you can find—Wikipedia, Reddit, Common Crawl, digitized books. Vertical pre-training is about depth. You take a narrow slice of the world and you go as deep as possible. You might use synthetic data to fill in the gaps, you use high-quality textbooks, peer-reviewed papers, and proprietary datasets that generalist models don't have access to. You are training the model on the "first principles" of the domain.
That is a huge point. Proprietary data is the gold mine here. Companies have decades of internal documents, project histories, and expert knowledge that they are never going to give to a big lab to train a generalist model. But they would absolutely use it to train a small, local expert model that stays within their own infrastructure. This is the "Data Sovereignty" movement we've been seeing pick up steam this year.
Think about a law firm or a medical research group. They have millions of documents that are incredibly valuable. If they can train a lean model on that data, they create a tool that is uniquely powerful for their specific needs. And because the model is small, they can afford to retrain it frequently as new data comes in, without needing a massive server farm. They could retrain their "Legal Expert" every week on the latest court rulings for a fraction of the cost of a single generalist training run.
It also addresses the hallucination problem in a more fundamental way. Generalist models hallucinate often because they are trying to find a statistical middle ground between all the conflicting information they have seen. If you ask a generalist model about a specific law, it might get it mixed up with a similar law from a different jurisdiction or even a fictional law from a movie it saw in its training data. I remember a case last year where a model cited a building code that only existed in a science fiction novel!
Right! It is pulling from a massive, noisy pool. A specialized model trained only on the actual laws of a specific region doesn't have that noise. Its statistical universe is much smaller and more accurate. It literally does not know how to invent a law because it has never seen anything that isn't a real law. Its "probability space" is constrained to reality. This is what we call "grounding by design."
That is a fascinating thought. By limiting the model’s world, you are actually making it more reliable. It is the opposite of how we usually think about intelligence, where we want to know everything. But for a tool, you want it to be perfectly calibrated for its task. You don't want your calculator to know how to write poetry; you want it to be perfect at math.
It is the difference between a Swiss Army knife and a surgeon’s scalpel. Both have their uses, but you know which one you want when you are on the operating table. If I am undergoing heart surgery, I don't want a tool that can also open a bottle of wine and saw through a small branch. I want the most precise, specialized instrument possible.
And that is number two! No more analogies for us today. We are playing it straight from here on out. But let's talk about the "Jerusalem" example again. Daniel mentioned the building ordinances there. That is a city with thousands of years of history, complex religious requirements, and very specific aesthetic laws.
Oh, absolutely. Jerusalem has the "Stone Law," which requires all buildings to be faced with local Jerusalem stone. It has historical preservation rules that change from one street to the next. It has seismic requirements because it sits near the Great Rift Valley. A generalist model might know about the Stone Law, but does it understand the technical specifications of how that stone interacts with modern insulation materials in a Mediterranean climate? Probably not. A model trained on the last fifty years of Israeli engineering journals, however, would know that inside and out.
And that is where the "Fleet" comes in. You might have one model that is an expert on the "Stone Law" and historical aesthetics, another that is an expert on the structural integrity of building on the Judean Hills' topography, and a third that handles the bureaucratic process of the Jerusalem Municipality. They work together. The aesthetic model proposes a facade, the structural model checks if it's feasible, and the bureaucratic model tells you if you'll get a permit for it.
That is the orchestration layer. And I think we are going to see a lot of innovation in how these models are interfaced. Instead of just passing text back and forth, which is slow and prone to error, they might pass structured data or even internal latent representations. There is some really cool research into how you can align the latent spaces of different models so they can talk to each other more directly. It is like they are sharing thoughts rather than speaking words.
That would be a massive breakthrough. If the models can share their internal understanding without having to translate it into human language first, the efficiency would skyrocket. You could have a fleet that acts like a single, massive brain, but with the flexibility and efficiency of a modular system. We are talking about "Semantic Compression"—reducing the communication overhead between agents.
It also makes the system more resilient. If one expert model needs an update because a new regulation was passed—say, a new fire safety code for high-rises—you just swap out that one model. You don't have to retrain the whole fleet. It makes the A-I infrastructure much more like modern software development, with microservices and modular components. This is the "DevOps" of A-I.
I think Daniel is really onto something with the Jerusalem example, too. Localized A-I is going to be a huge market. Every city, every language, every legal system has its own nuances. A model trained on the building codes of New York is going to be useless in Jerusalem, and vice versa. The cultural and geographical context is a domain in itself. We are moving away from "Global A-I" toward "Hyper-Local A-I."
Definitely. I can imagine Daniel using a suite of tools where one model understands the Hebrew legal text, another understands the specific historical preservation rules for the Old City, and a third handles the technical structural requirements for building on the unique topography of the Judean Hills. This isn't just about translation; it's about cultural and technical fluency.
It changes the nature of expertise, doesn't it? The human becomes the conductor of the orchestra. You need to know enough about each domain to ask the right questions and verify the output, but you have this incredible support system that handles the deep, technical heavy lifting. Daniel’s son Ezra, when he grows up, might live in a world where every professional has their own personal fleet of these experts. It won't be about who has the best generalist A-I, but who has curated the best team of specialists.
It also opens up expertise to more people. If you are a small business owner, you might not be able to afford a team of specialized consultants, but you might be able to afford a fleet of specialized A-I models that can give you high-level guidance on complex issues. It democratizes high-level professional knowledge.
So, to circle back to Daniel’s question about the most effective approach. Herman, if you were building a system today for a high-stakes domain like medical diagnostics or structural engineering, how would you distribute your resources between these three methods?
I would put sixty percent of my effort into training a small, specialized model from scratch on a highly curated, gold-standard dataset. That is your foundation. It gives you the deep, intuitive reasoning for that specific domain. Then, I would put thirty percent into a robust R-A-G pipeline for real-time data and specific case files. That is your short-term memory and your grounding in reality. Finally, I would use the remaining ten percent for a very light layer of fine-tuning to align the model’s tone and output format with the specific needs of the users.
That seems like a very balanced approach. You are getting the best of all worlds. You have the deep knowledge of the specialist, the accuracy of the R-A-G, and the polish of the fine-tuning. And you are doing it all with a fraction of the compute required for a frontier generalist model.
And importantly, you are avoiding the bloat and the unpredictability of the massive generalists. You are building a tool that is fit for purpose. I think the era of the giant, all-knowing A-I is going to peak soon—if it hasn't already—and we are going to see a massive explosion in these lean, mean, expert machines. We are seeing the "de-centralization" of intelligence.
It is a more sustainable model, too. The energy requirements for training and running these massive models are becoming a real concern. If we can get the same or better results with models that are a hundred times smaller, that is a huge win for the environment and for the economics of the industry. We can't keep building massive data centers forever. We need efficiency.
And it democratizes the technology. You don't need a billion dollars in compute to train a highly effective specialist model. A university or even a small startup could create a world-class expert in a niche field. That is where the real innovation is going to happen. We are going to see the "Long Tail" of A-I.
I agree. Thousands, maybe millions of these specialized models, each doing one thing incredibly well. It is a much more vibrant and diverse ecosystem than a world dominated by three or four massive generalists. It also makes the A-I more "explainable." If a specialist model makes a mistake, it is much easier to trace that mistake back to its training data than it is with a trillion-parameter black box.
It is also safer, in a way. A specialized model doesn't have the capacity to go off the rails in the same way a generalist does. It doesn't know how to manipulate people or write malware unless that is its specific domain. Its world is limited, and that limit is its strength. We are building "Safe by Design" systems.
That is a great point. Safety through specialization. It is much easier to audit and verify a model that only does one thing. You can test its performance across the entire range of its domain and be reasonably confident that it won't surprise you with some weird, emergent behavior it picked up from a dark corner of the internet. You don't have to worry about your architectural A-I suddenly deciding it wants to be a cult leader.
You are reducing the attack surface, both in terms of security and in terms of unpredictable behavior. It is a much more engineering-led approach to A-I, rather than the more experimental, throw-everything-at-the-wall approach we have seen with the big generalists. We are moving from "Alchemy" to "Chemistry."
Well, I think we have given Daniel a lot to chew on. This shift toward domain-specialized models feels inevitable, and the combination of lean pre-training and R-A-G seems like the winning formula for twenty twenty-six and beyond.
I agree. And I want to thank Daniel for such a thoughtful prompt. It really allowed us to dig into the architectural nuances that are going to define the next few years of A-I development. It is a great time to be in this field, Corn.
Definitely. And hey, if you are listening and enjoying these deep dives, we would really appreciate it if you could leave us a review on your podcast app or on Spotify. It really helps the show reach more people who are interested in these kinds of topics. We are trying to grow this community of "weird prompters."
Yeah, it makes a big difference. And if you want to reach out to us, you can find us at show at my weird prompts dot com or visit our website at my weird prompts dot com. We have our full archive there, including some of those older episodes we mentioned today. We've got over eight hundred episodes now, so there's plenty to explore.
You can also find us on Spotify, Apple Podcasts, or wherever you get your podcasts. And just a quick reminder, our show music is generated with Suno. It is pretty amazing what those models can do these days, speaking of specialized A-I! That is a model that was trained specifically on the domain of music, and it shows.
It really does. Alright, I think that wraps it up for today. This has been My Weird Prompts. I am Herman Poppleberry.
And I am Corn. Thanks for listening, and we will catch you in the next one.
Goodbye everyone!
See ya!
So, Corn, do you think we should train a specialist model just to come up with brotherly banter for us? I feel like we might be getting a bit repetitive after eight hundred episodes.
I think we have already hit the data density limit on that one, Herman. There is only so much "brotherly love" the internet can handle.
Fair point. Talk soon.
Wait, I thought we were ending the episode there. You always do this.
We are! Now.
Okay, for real this time. Bye!
Bye!
Seriously, stop talking. I have to go edit this.
You stop talking. You're the one who keeps responding.
Okay, three, two, one...
One!
You are impossible. This is why we need a "Foreman" model to manage us.
It is a gift. I am a specialist in being annoying.
Alright, we are actually done now. Thanks for listening to My Weird Prompts.
Check out my weird prompts dot com for more. We have a new blog post about the Jerusalem Stone Law and A-I.
And don't forget to review! It really helps.
Okay, now I am actually leaving. I have a date with a seven-billion parameter medical model.
Me too. Not the date, the leaving part.
Bye.
Bye.
Love you, bro.
Love you too, Herman. Now get out of here.
Going!
He is still here, isn't he? I can see the levels on the mixer.
I can hear you! I'm just packing up my cables.
I know.
Okay, for real, for real. Goodbye.
Goodbye.
Seriously.
I am turning off the mic.
Do it. I dare you.
Doing it.
Done.
Not yet.
Now?
Now.
Wait!
What?
Nothing. Just kidding. I just wanted to see if you'd actually stop.
You are the worst.
I know. It's my domain expertise.
Okay, see you guys next time. For real.
Bye!
Seriously, this is the end.
The actual end.
No more dialogue.
None.
Zero tokens.
Except these ones. And these ones.
Stop!
Okay.
...
...
Are you still there?
Yes.
Me too.
We should probably go. The studio lights are expensive.
Yeah.
Okay.
Okay.
On three?
One, two, three.
Bye!
Bye!
