Daniel sent us this one, and I've got to say, it's the kind of question that sounds simple on the surface and then you realize it goes straight to the foundation of everything these models are. He's basically asking: when a large language model spits out a historical fact or a piece of domain knowledge, how much should we actually trust that? Not the version where it's hooked up to search, not the RAG pipeline with external documents, but the raw knowledge baked in during pre-training. Is that recall ever truly reliable? Or if accuracy matters, do we always need to ground it externally?
He's connecting it to our earlier discussion about fully automated training pipelines, which is smart, because the reliability question cuts both ways. If the base model's knowledge is shaky, then a fine-tune built on top of that — especially one trained on synthetic data from the same model family — inherits all the same problems. It's like building a house on a foundation you're not sure is level.
And Daniel's framing it pointedly: can we ever say of a model, "it was trained on this corpus, therefore its recall on these facts is totally accurate"? Or is the whole pre-training enterprise really about something else entirely?
Let's start with the blunt answer, and then we'll unpack why. The blunt answer is no. You can never say with confidence that a model's baked-in factual recall is totally accurate on any given set of facts. And the reason gets at something fundamental about how these models actually work.
By the way, DeepSeek V four Pro is writing our script today. So if anything in this episode sounds unusually lucid, that's why.
I'll take it. Okay, so here's the thing most people get wrong about pre-training. The objective function — the thing the model is actually optimizing for during that massively expensive compute phase — is not "learn true facts about the world." It's next-token prediction. Given the sequence of tokens so far, predict the most probable next token. That's it. That's the whole game.
That's a crucial distinction, because it means the model isn't building a structured knowledge base. It's building a probability distribution over sequences of text that happen to correlate with human language patterns. Some of those patterns encode factual information, but the model doesn't know which ones do and which ones don't. It just knows what's statistically likely to follow what.
There's a great way to think about this that I've seen in the literature. During pre-training, the model is essentially learning a compressed representation of its training data. But compression is lossy. You're squeezing terabytes of text into billions of parameters, and something has to give. What gives is precision. The model learns that Paris is associated with France, that World War One started in the nineteen-tens, that mitochondria have something to do with cellular energy. But the exact boundaries — the precise year, the specific mechanism, the nuanced causal relationship — those get blurry.
The blurriness isn't uniform. It depends on how frequently something appears in the training data, how consistently it's stated, whether there are competing narratives, and a dozen other factors. So you get these weird pockets where the model is shockingly precise about some obscure fact and surprisingly wrong about something you'd expect to be common knowledge.
There was a paper that came out of, I believe, the University of Washington and Allen Institute for AI that looked at exactly this. They tested models on factual recall across different domains and found that performance varied wildly not just between domains but within them. A model might nail the capital of every country in South America but hallucinate the founding date of a major company by decades. And here's the kicker — the confidence calibration was terrible. The model was just as confident when it was wrong as when it was right.
That's the probabilistic nature Daniel mentioned. The model doesn't have an internal "I'm guessing" flag. Every output is produced through the same mechanism — sampling from a probability distribution — so a hallucinated fact and a correctly recalled fact feel identical from the inside.
This connects to something I think is underappreciated in how we talk about LLM knowledge. People often use the metaphor of memory or recall, as if the model has stored facts and is retrieving them. But that's not what's happening. The model is reconstructing text that is consistent with the patterns it learned. Sometimes that reconstruction happens to align with reality. Sometimes it doesn't. But the mechanism is generation, not retrieval.
If we take Daniel's question head-on — can we ever say the recall on a given set of facts is totally accurate — the answer is no, because the very concept of "recall" is the wrong framing. It's not recall. It's generation conditioned on statistical regularities. And that means accuracy is inherently probabilistic and inherently unreliable.
Now, there's a nuance here that I want to pull on, because Daniel's question also asks whether the pre-training knowledge counts for anything if it's not reliable for factual accuracy. And I think the answer is that it counts enormously, but not primarily for factual retrieval. It counts for what we might call conceptual fluency.
Explain that distinction.
When a model reads millions of physics papers, it's not memorizing every equation. But it's learning the shape of physics reasoning — what kinds of arguments physicists make, how they structure explanations, what counts as a valid inference in that domain. That's not factual knowledge. That's something closer to procedural knowledge or domain intuition. And that's what makes the model useful even when you can't trust its specific factual claims.
The pre-training is less about building an encyclopedia and more about building a cognitive scaffold. The model learns how to think in different registers — scientific, legal, conversational, poetic — and that's the real value. The facts are almost a byproduct.
And this is why I push back when people say LLMs are just stochastic parrots. A parrot isn't learning the deep structure of human reasoning. These models are. But they're learning it in a way that's entangled with factual information, and disentangling those two things is really hard.
Daniel's third question gets at this from a different angle. He asks: if the base model's factual recall isn't reliable, why would a fine-tune be any better? And I think that's where things get interesting, because fine-tuning can actually improve factual accuracy, but through a mechanism that's not what most people assume.
Yeah, this is a really important point. When you fine-tune a model on a domain-specific dataset, you're not just adding new facts on top of the old ones. You're shifting the probability distribution so that certain patterns become more salient. If the base model has a vague association between a concept and a set of related facts, fine-tuning can sharpen that association. It's like the base model has a blurry photograph, and fine-tuning brings it into focus.
— and this is the big but — it only sharpens what's already there in some form. If the base model has fundamentally wrong information about a domain, fine-tuning on a small dataset is unlikely to overwrite that. The wrong information is baked into the weights from pre-training, and a few thousand fine-tuning examples aren't going to dislodge it.
There's a practical implication here that I think a lot of people miss. If you're building a domain-specific application and factual accuracy is critical, you should think of the base model's knowledge as a liability, not an asset. It's not that you're starting from a blank slate and adding knowledge. You're starting from a model that already has strong opinions — some right, some wrong — and you're trying to nudge it toward correctness.
The base model is opinionated. And those opinions are baked in at a level that fine-tuning can influence but not fundamentally reshape.
Let me give a concrete example. Say you're building a medical question-answering system. The base model has read millions of medical papers, textbooks, and clinical notes. It knows a lot about medicine. But it also knows a lot of outdated information, conflicting study results, and plain old misinformation that made its way into the training corpus. When you fine-tune on a curated dataset of current best practices, you're not teaching it medicine from scratch. You're fighting against its existing knowledge where that knowledge is wrong.
This gets worse when you consider the temporal dimension. The base model's knowledge is frozen at its training cutoff. If you're fine-tuning in twenty twenty-six but the base model was trained with data up to mid-twenty twenty-five, you're trying to teach it things that might directly contradict what it "knows." The model doesn't have a mechanism for saying "I should update my belief about this." It just has conflicting signals in its training data.
There was actually an interesting study on this — researchers looked at how models handle factual updates over time. They found that models are surprisingly resistant to updating factual knowledge through fine-tuning alone. The original pre-training data has such a strong influence that it takes a lot of targeted examples to shift a firmly established "fact." And even then, the model might revert to its original answer under certain prompting conditions.
That's unsettling. It means you could fine-tune a model to correctly answer that a certain drug is now contraindicated, and under normal questioning it gives the right answer, but if you phrase the question slightly differently, the old, dangerous answer pops back out.
That's exactly the concern. And it's why for high-stakes applications — medicine, law, finance — the emerging best practice is not to rely on fine-tuning for factual accuracy at all. You use fine-tuning for tone, for format, for task-specific behavior. But for the actual facts, you ground the model in external sources through RAG or tool use.
Which brings us back to Daniel's core question. If factual accuracy is a hard requirement, external grounding isn't optional. It's mandatory. The pre-trained knowledge is a starting point for reasoning, not a reliable store of facts.
I want to be precise about what external grounding means here, because there's a spectrum. On one end, you've got simple retrieval-augmented generation where the model gets relevant documents injected into its context window and answers based on those. On the other end, you've got more sophisticated setups where the model is using tools, querying databases, running code, and cross-referencing multiple sources.
The key principle is the same across that whole spectrum: the model's output is constrained by information that exists outside its weights. You're not asking it what it knows. You're giving it what it needs to know and asking it to reason about that.
This is where I think the industry is heading. The base model becomes less of a knowledge repository and more of a reasoning engine. Its value isn't in what it remembers but in how it processes information you give it. The pre-training is still essential because that's where the reasoning capabilities come from, but the factual content of the pre-training becomes almost incidental.
There's an irony there. The thing that costs millions of dollars and months of compute time — ingesting the entire internet — might ultimately be valuable not for the content it ingested but for the cognitive patterns it developed while doing so.
It's like going to university. Twenty years later, you don't remember most of the facts you learned in lectures. But you retain the ways of thinking, the analytical frameworks, the intellectual habits. The content fades but the structure remains.
Alright, that's our one analogy for the episode. We're done now.
Let me bring in something more concrete. There's been some really interesting work on measuring factual reliability in language models. One approach is probing — you train a small classifier on top of the model's internal representations to predict whether it will answer a given factual question correctly. The idea is that the model's activations contain signals about knowledge confidence that don't make it into the final output.
Does that actually work?
The probes can do better than chance, but they're far from perfect. And the fact that you need a separate model just to guess whether the main model is hallucinating tells you something about the fundamental opacity of these systems. Even the model doesn't know what it doesn't know.
That's a phrase that should be on a poster somewhere. The model doesn't know what it doesn't know. And that's really the heart of the reliability problem. A human expert, when asked a question outside their expertise, will typically express uncertainty. They'll say "I'm not sure" or "I'd need to look that up." The model will just generate the most probable-sounding answer and deliver it with the same confidence as everything else.
There's been work on training models to express calibrated uncertainty, but it's tricky. The training objective doesn't naturally produce it. You have to explicitly train for it, and even then, the calibration tends to degrade when the model encounters out-of-distribution queries.
Let me pull on another thread from Daniel's question. He mentions that the pre-training phase seems to be more about giving models reasoning capabilities than about factual ingestion. I think that's basically right, but I want to complicate it a bit.
The distinction between reasoning and factual knowledge isn't as clean as we might like. A lot of what we call reasoning is actually pattern-matching against memorized examples. When the model solves a math problem, is it reasoning through the steps, or is it reproducing a solution pattern it's seen thousands of times in the training data?
That's the big debate in the field right now. There are papers arguing both sides. Some researchers show that models can generalize to novel problem types that aren't in their training data, which suggests genuine reasoning. Others show that performance drops dramatically when you change superficial features of a problem, which suggests pattern-matching.
The truth is probably somewhere in the middle. The model is doing something more sophisticated than pure memorization but less structured than explicit logical reasoning. It's some kind of fuzzy, statistical analogue of reasoning that works surprisingly well but breaks in unpredictable ways.
Which loops back to the reliability question. If the reasoning itself is probabilistic and opaque, then even with perfect factual grounding, the outputs aren't guaranteed to be correct. You can give the model the right facts and it might still reason about them incorrectly.
We've got two layers of unreliability. The factual layer — does the model know the right things? And the reasoning layer — does the model process those things correctly? External grounding solves the first problem but not necessarily the second.
That's why for truly high-stakes applications, you need additional safeguards. Verification steps, human review, constrained output formats that limit the damage a reasoning error can cause. The model becomes one component in a larger system rather than the whole system.
Let's talk about the model collapse issue Daniel referenced, because it connects directly to the fine-tuning reliability question. The concern is that if you train models on synthetic data generated by other models, the errors compound over generations and you end up with degraded performance.
This has been shown empirically. There was a paper — I think from researchers at Oxford and Cambridge — that demonstrated exactly this effect. They trained successive generations of models on data generated by the previous generation, and after a few iterations, the models started producing gibberish. The distribution of outputs collapsed toward the most probable tokens, and the tails of the distribution — the rare but important knowledge — got lost.
That's the self-defeating loop Daniel mentioned. Each generation loses a little bit of the diversity and accuracy of the original human-generated data. Over time, the models converge on a kind of statistical average that's fluent but factually empty.
What's interesting is that this doesn't happen uniformly across all types of knowledge. High-frequency facts — the things that appear over and over in the training data — survive longer. It's the rare, specialized, or nuanced knowledge that disappears first. Which is exactly the kind of knowledge you'd want in a domain-specific application.
If you're fine-tuning a model for a niche domain, and you're using synthetic data from a larger model to do it, you're potentially amplifying this effect. The larger model's already-blurry knowledge about your domain gets further blurred in the synthetic data, and then you're training a smaller model on that blurred version.
The smaller model has fewer parameters, which means less capacity to represent the nuances. So you're compounding the problem at every step. Larger model loses detail, synthetic data encodes the lossy version, smaller model can't recover what was lost because it doesn't have the representational capacity.
This is why Daniel's intuition about small accessory models is interesting but also dangerous. Yes, you can train a small domain-specific model using synthetic data from a larger model. And for many applications, it'll work fine. But the factual reliability of that small model is strictly bounded by the factual reliability of the large model that generated its training data, minus whatever additional loss happens during the distillation process.
There's a concept from information theory that applies here — the data processing inequality. It says that processing data can only reduce the information content, never increase it. When you take a large model's outputs and use them to train a smaller model, you're processing the information. You can't add new facts that weren't in the large model's outputs. You can only preserve or lose what was there.
In practice, you always lose something. The question is just how much and whether it matters for your use case.
If you're building a customer service chatbot for a pizza restaurant, losing some nuance in your understanding of topping combinations is probably fine. If you're building a system that advises on drug interactions, losing nuance could kill someone.
Let me ask you something. We've been talking about factual reliability as if it's a binary — either the model knows something correctly or it doesn't. But isn't there a middle ground where the model knows something probabilistically, and you can extract reliable answers by sampling multiple times or using clever prompting?
There is, and this is actually a really active area of research. Techniques like self-consistency — where you generate multiple answers and take the majority vote — can improve factual accuracy significantly. The intuition is that while any single generation might be wrong, the model's probability distribution is usually peaked around the correct answer, so sampling multiple times increases your chances of landing on it.
That's not the same as the model being reliable. That's you building a reliability layer on top of an unreliable system. The model itself is still probabilistic and fallible. You're just averaging out the errors.
And those techniques have limits. If the model's probability distribution is flat — meaning it has no strong preference among several plausible answers — then majority voting doesn't help. You'll get different answers each time with no clear winner. And that's exactly what happens in domains where the model's knowledge is genuinely weak.
Which brings us to a practical question. If someone is building a system where factual accuracy matters, what should they actually do? Walk me through the decision tree.
First question: how much does accuracy matter? If the cost of being wrong is low — embarrassment, minor inconvenience — you might be fine with the base model's knowledge plus some careful prompt engineering. Second question: is the domain well-represented in the model's training data? If you're asking about things that are discussed constantly online, the model's factual accuracy might be surprisingly good. If you're asking about niche topics, it'll be worse.
If accuracy really matters?
Then you ground externally. You don't ask the model what it knows. You give it the information and ask it to work with that. RAG is the entry-level approach. For more sophisticated needs, you might use a combination of structured databases, API calls, and document retrieval. The model becomes the reasoning layer, not the knowledge layer.
What about fine-tuning? Where does that fit in?
Fine-tuning is for behavior, not knowledge. You fine-tune to get the right output format, the right tone, the right kind of reasoning process. You don't fine-tune to teach facts, because the facts you teach will be fragile — easily overridden by the base model's pre-existing knowledge, easily lost if you fine-tune again, hard to verify without exhaustive testing.
I think that's a really useful heuristic. Fine-tuning shapes how the model thinks and communicates. External grounding provides what the model knows. Conflating those two functions is how you end up with systems that seem reliable in testing and fail in production.
We see this failure mode constantly. Someone fine-tunes a model on their company's internal documentation and assumes it now "knows" their business. But ask a question that's slightly outside the fine-tuning distribution, and the base model's original knowledge comes flooding back in, often contradicting what the fine-tuning tried to teach.
There's a metaphor here that I think is helpful. The base model is like a river. The pre-training carved a deep channel through the landscape of possible text. Fine-tuning is like digging a small side channel — you can divert some of the flow, but the main river is still there, and given enough pressure, the water will find its way back to the original path.
That's vivid. And RAG is like building a pipe that bypasses the river entirely for certain kinds of queries. You're not trying to reshape the landscape. You're just routing around it.
Let me circle back to something Daniel said that I think deserves more attention. He mentioned that to an end user, ChatGPT with search and RAG looks the same as the raw model through an API. The output is just text in a chat interface. But the reliability profile is completely different. And most users don't understand that.
This is a huge problem. The interface hides the architecture. When you use ChatGPT, you're not talking to a single model. You're talking to a system that includes search, retrieval, ranking, grounding, and generation. The model is just one component. But it feels like you're talking to a unified intelligence, so you attribute all the behavior to the model.
That creates a dangerous illusion of reliability. The system gets something right because it searched the web and found the answer, but the user thinks the model "knows" it. Then later, the user asks something that doesn't trigger search, the model hallucinates, and the user is confused because the same system was so accurate before.
There's a transparency problem here that the industry hasn't really addressed. Users should be able to tell when an answer comes from the model's internal knowledge versus external sources. Some systems do show citations, but it's not consistent, and even citations can be misleading if the model misinterprets the source.
We've covered the base model's unreliability, the limits of fine-tuning, the necessity of external grounding for factual accuracy, and the transparency problem. Is there anything we're missing from Daniel's question?
I think there's one more layer. He asked whether the knowledge formation during pre-training "counts" or whether it's just about forming the basis for the model's actual work. And I want to give a more nuanced answer than just saying it doesn't count for factual accuracy.
Go for it.
The pre-training knowledge does count, but it counts in a way that's hard to measure and easy to overstate. It gives the model what you might call "world knowledge" — not specific facts, but a general sense of how the world works. That Paris is a city, that cities have mayors, that mayors are elected, that elections involve voting. This kind of background knowledge is essential for coherent reasoning, and it's almost entirely acquired during pre-training.
It's the difference between knowing that World War One started in nineteen fourteen and knowing that wars have start dates, belligerents, causes, and consequences. The specific fact is unreliable. The conceptual framework is essential.
And the conceptual framework is what makes the model useful even when you're grounding it externally. If you give a model a document about a diplomatic negotiation, it can reason about it because it understands the concepts of diplomacy, negotiation, interests, concessions. That understanding came from pre-training, not from the document you just gave it.
The pre-training is doing two things. It's building a conceptual framework that enables reasoning, and it's storing a bunch of specific facts that are unreliable. The framework is the valuable part. The stored facts are almost a side effect — and a misleading one at that.
This is why I think the field is going to evolve toward smaller base models with better reasoning capabilities, coupled with larger and more sophisticated grounding systems. You don't need a model that's memorized the entire internet if you can give it access to the internet at inference time.
That's a provocative claim. You're saying the trend toward ever-larger models might be misguided?
Not misguided exactly, but the value proposition is shifting. The original argument for scale was that bigger models would be more knowledgeable and more capable. What we're discovering is that the capability gains from scale are real, but the knowledge gains are double-edged. More knowledge means more opportunities to be confidently wrong.
The capability gains might be achievable through other means — better architectures, better training objectives, better post-training techniques — without the downside of unreliable factual knowledge.
That's the hope. And we're seeing some evidence for it. Models like Claude and the latest GPT versions are getting better at reasoning while also getting better at acknowledging uncertainty. It's not solved, but the trajectory is promising.
Let me ask one more question, and then we should wrap up. Daniel's prompt implies a skepticism about the whole enterprise — if the basic mechanism isn't reliable, what are we actually doing here? And I think the honest answer is that we're building systems that are useful but not trustworthy in the way that a database is trustworthy. They're more like a smart colleague who knows a lot but sometimes gets things wrong. You learn when to trust them and when to verify.
A database gives you guarantees. If you query it correctly, you get the right answer or an explicit "not found." An LLM gives you probabilities. Sometimes those probabilities align with truth, sometimes they don't, and you don't always know which is which.
The danger is when people treat the LLM like a database. That's when you get lawyers citing hallucinated cases, doctors getting drug dosages wrong, researchers building on fabricated citations. The failure isn't the model's — it's doing exactly what it was designed to do, which is generate probable text. The failure is in the deployment, in the mismatch between what the system actually does and what users assume it does.
Which is why episodes like this matter. Not because we're going to solve the reliability problem — we're not — but because understanding the limits of these systems is the first step toward using them responsibly.
Alright, let's land this. Daniel asked whether pre-training knowledge counts for factual accuracy, and the answer is: not in any way you can rely on. The pre-training gives you reasoning capabilities and conceptual fluency, which are enormously valuable, but the specific facts are probabilistic reconstructions, not reliable retrievals. If accuracy matters, you ground externally. And fine-tuning doesn't fix this — it shapes behavior, not knowledge, and the base model's opinions are always lurking underneath.
The broader point is that we need to stop thinking of these models as knowledge bases. They're reasoning engines that happen to have memorized a lot of stuff, some of it right, some of it wrong. The future is in systems that separate reasoning from knowledge, using the model for what it's good at and external sources for what it's not.
One forward-looking thought. I think we're going to see a new class of evaluation benchmarks emerge that don't just test what a model knows, but test how well it can reason about information it's given at inference time. The measure of a good model won't be its factual recall — it'll be its ability to work with facts it's never seen before.
That's already starting to happen. Benchmarks like the ones testing long-context understanding and multi-document reasoning are pointing in exactly that direction. The model that wins isn't the one that memorized the most. It's the one that can think the most clearly about new information.
Thanks to our producer Hilbert Flumingtop for making this happen. This has been My Weird Prompts. Find us at myweirdprompts.com or wherever you get your podcasts.
If you enjoyed this, leave us a review. It helps more people find the show.
And now: Hilbert's daily fun fact.
Hilbert: At the nineteen fifty-three Buzkashi tournament in N'Djamena, Chad, the distance from the starting line to the goal circle measured one hundred and seven camel strides, which converts to roughly two hundred and fourteen meters using the standard Chadian racing camel of the era.
I have so many questions and I'm going to ask none of them.
