Imagine you are a high-priced corporate lawyer, and you have spent the last six hours feeding every single deposition, contract, and case file from a massive acquisition into an AI research assistant. You ask it a hyper-specific question about a regulatory filing from nineteen ninety-eight, and it gives you a perfect, three-paragraph summary, complete with a citation. You’re thrilled. You go to look up that case, and it doesn't exist. Not in your files, not in the public record, nowhere. The AI didn't just make it up out of thin air; it "remembered" a similar sounding case from its original training data and decided that was more relevant than the actual documents you gave it. This is the ghost in the machine we're talking about today.
It is the ultimate nightmare for compliance officers. I am Herman Poppleberry, and today we are tearing into the technical illusion of the "constrained" AI model. We have all seen the marketing for tools like Google’s NotebookLM or custom corporate RAG stacks. They promise a "walled garden" where the AI only knows what you tell it. But as we are going to see, those walls are a lot shorter and a lot more porous than most CTOs want to admit. By the way, today's episode is powered by Google Gemini three Flash, which is actually a perfect meta-commentary on the topic because we are using a massive, world-spanning model to discuss how to keep models from using their world-spanning knowledge.
Today's prompt from Daniel is about exactly that. He wants to know if it is actually possible to force a model to stay within a defined corpus—like a company's internal wiki or a legal discovery set—and ignore everything it learned during pre-training. He’s looking at the compliance risks when these models "go rogue" with unverified info. And honestly, Herman, I feel like "strictly observed guardrails" is one of those phrases that sounds great in a boardroom but makes engineers break out in a cold sweat.
Because it’s a fundamental misunderstanding of how these models work. You can’t just flip a switch and turn off the "internet" part of the brain while keeping the "reasoning" part active. They are the same part. When you use a model to summarize a document, it’s using its pre-trained understanding of English, logic, and structure to do that. You can't have the logic without the latent knowledge associated with it.
But wait, if I tell the model in the system prompt, "You are a strict research assistant. Use ONLY the provided context," doesn't that create a logical barrier? I mean, we see these instructions in almost every enterprise AI implementation. "Do not use outside knowledge." Does the model just... ignore that?
It doesn't ignore it so much as it struggles to distinguish between "knowing how to speak" and "knowing what is true." Think of it like a professional translator who spent twenty years living in Paris. If you give them a document to translate that says "The Eiffel Tower is in London," their brain is going to scream that it's wrong. Even if you tell them to be a literal translator, their internal bias toward the truth—or what they think is the truth—will color how they phrase the translation. They might subconsciously "correct" it or add nuance that wasn't in the source text because their internal weights are simply too heavy to ignore.
So, let’s define the stakes here before we get into the weeds. If I'm a bank and I build a customer service bot, I want it to talk about my mortgage rates, not the rates from a competitor that it saw on Reddit three years ago during its training phase. If it hallucinates a better deal for a customer because it thinks its internal "knowledge" is superior to the document I fed it this morning, that’s not just a glitch; that’s a potential multi-million dollar class-action lawsuit.
Or a regulatory fine. We saw data from late twenty twenty-five showing financial services firms facing average fines of two point three million dollars for AI-related compliance violations. Most of those weren't "the AI turned evil." It was "the AI gave advice that wasn't in the approved manual." To understand why this happens, we have to look at how Retrieval-Augmented Generation, or RAG, actually functions. It’s a three step process: Retrieval, Augmentation, and Generation.
Right, the "Retrieval" part is essentially the AI doing a quick search through your private files. The "Augmentation" is sticking those search results into the prompt. And the "Generation" is the model writing the answer. But here is the rub: the model is still the one doing the writing. It’s not a search engine; it’s a creative writer looking at some notes you gave it.
And that is where the "Superior Knowledge" problem kicks in. Inside the transformer architecture—the heart of these models—there is a mechanism called attention. When the model is generating a word, it’s looking at two things. It’s looking at the "context" you provided in the RAG window, and it’s looking at its own internal "weights"—everything it learned during the months it spent reading the entire public internet.
So it’s like a student taking an open-book test. You give them the textbook, but they also have their own memory. If the textbook says the sky is green for the sake of a logic puzzle, but the student "knows" the sky is blue, a really "smart" student might think the textbook has a typo and just write "blue" anyway.
That is a perfect way to put it. In technical terms, we call this the "Truth Conflict." A study from Stanford’s Institute for Human-Centered AI in early twenty twenty-five found that sixty-seven percent of RAG systems showed instances where the model’s internal knowledge overrode the retrieved context. The more "capable" the model is—think GPT-four or Gemini one point five Pro—the more "opinionated" it becomes. It has high confidence in its internal weights because those weights have been reinforced billions of times.
I love the idea of a "confident" AI just deciding our internal documents are trash. "I'm sorry, Corn, I know your company's policy says employees get three weeks of vacation, but I've read ten thousand labor law blogs that say four weeks is the standard, so I'm going to tell the new hire they get four." It’s basically a digital gaslighting machine at that point.
It really is. And look at Google’s NotebookLM. It’s probably the most high-profile example of a "constrained" system. It’s designed to answer questions only based on the sources you upload. When you use it, it even shows you citations. But even there, the constraints are "soft." They are implemented through system prompting—telling the model "be a good boy and only look at these PDFs"—and through post-generation checks. But the underlying model still has all that pre-trained data lurking in its neurons.
So what you're saying is there is no "Technical Hard Wall." There is no way to physically isolate the model from its training data once it’s been trained.
Not without retraining a model from scratch on only your data, which would be useless because it wouldn't know how to speak English or follow instructions. You need the "base" knowledge to provide the "reasoning" capability. The problem is that the facts are baked into the reasoning. If a model knows how to explain a complex medical procedure, it’s because it saw a million examples of that procedure. If your company has a slightly different, proprietary way of doing that procedure, the model has to actively suppress its "instinct" to give the common answer in favor of your specific one.
That sounds like a recipe for "Knowledge Leakage." I've seen this happen in internal tools where the model starts using jargon that isn't in the source documents. It’s like the model is trying to be helpful by filling in the gaps. If the source document is silent on a specific detail, the model doesn't always say "I don't know." It thinks, "Well, I'm a helpful assistant, and I happen to know the answer to this from my training data, so I'll just slip that in there to be a pal."
And that "pal" is going to get you sued. Let’s look at a case study. There was a financial services firm—we won't name names, but this made the rounds in the industry—that built an internal research tool for their analysts. They fed it all their proprietary market research. An analyst asked about the regulatory outlook for a specific green energy credit. The internal documents were actually quite skeptical and pointed out some obscure legal hurdles. But the model, having been trained on thousands of optimistic tech-bro articles about green energy, gave a glowing, positive outlook and even cited a "regulation" that looked real but was actually a mashup of three different bills that never passed.
See, that’s the "Confidence Trap." The model doesn't sound like it's guessing. It sounds like an expert. And because it’s a RAG system, the analyst assumes the info is coming from the internal documents. They don't double-check. They just copy-paste that into a report for a client. Suddenly, the firm has issued a report based on a hallucinated regulation that contradicts their own internal research.
But Corn, think about the "why" here. Why does the analyst trust it? It's because the RAG system did pull the correct document. The document was in the context window. The AI just decided to "improve" upon it. This is a phenomenon researchers are calling "Inductive Bias Overpowering." The model’s internal probability for "Green energy is the future" was so high that it treated the skeptical internal document as an outlier to be smoothed over.
That’s terrifying. It’s like hiring a consultant who ignores your data because they read a different opinion on LinkedIn. But how does this play out in something more rigid, like a legal setting? If you give an AI ten thousand pages of discovery, and ask for a summary of a specific meeting, can it really hallucinate a person who wasn't there just because its training data says that person usually attends those types of meetings?
We've seen "Role Hallucinations" where a model "assumes" a CEO must have been in a meeting because, in ninety-nine percent of the corporate transcripts it read during training, the CEO is present. So even if the specific RAG context says the CEO was out sick, the model might include them in the summary. It's following a pattern, not a fact.
This is why companies are starting to realize that "Strict Grounding" is a bit of a myth. It’s more like "Strongly Encouraged Grounding." To get closer to a real constraint, you have to use a "sandwich" approach. You don't just give the model the documents. You have to have a second, smaller model—what we call a Natural Language Inference or NLI model—that acts as a fact-checker. Its only job is to look at the generated answer and the source documents and ask: "Is every single claim in this answer explicitly supported by these documents?"
That’s like having a grumpy editor standing over the writer’s shoulder. The writer wants to be creative and use their "outside knowledge," and the editor just keeps slapping their hand and saying, "Is it in the notes? No? Delete it." But that adds a lot of latency, doesn't it? And cost.
Huge latency. You’re basically running the AI twice for every single question. And even then, it’s not perfect. If the "editor" model is also an LLM, it can have the same biases! It might also think the hallucinated fact sounds "right" and let it slide. This brings us to the compliance and risk angle Daniel mentioned. In a regulated environment, "mostly accurate" is the same as "totally wrong."
Right, because in healthcare or finance, you aren't just looking for a summary. You are looking for a fiduciary-grade response. If a healthcare AI assistant references an outdated treatment protocol because that protocol was dominant in its twenty twenty-three training data, even though the twenty-six internal guidelines you uploaded say otherwise, you have a massive liability issue. The model "determined" its knowledge was superior because it had seen that outdated protocol ten thousand times, whereas it only saw your new guideline once in the RAG context.
It’s a weight issue. Ten thousand "votes" for the old way versus one "vote" for the new way. Even if you tell the model "this one vote counts for a million," the mathematical pathways in the transformer are still biased toward the old info. This is why some people are moving toward Small Language Models, or SLMs. Instead of using a giant model that knows everything about the history of the world and how to write poetry in the style of Emily Dickinson, you use a tiny model that barely knows how to tie its shoes, but you train it intensely on a specific domain.
It’s the "specialist" versus the "polymath" approach. The specialist is less likely to get distracted by what it saw on Wikipedia because it hasn't read Wikipedia. But the downside is that the specialist might not be as good at understanding the nuance of a complex question.
Wait, I promised I wouldn't say that. You're right, Corn. The trade-off is reasoning capability. The smaller the model, the less "intelligent" it feels. It might struggle with complex logic or multi-step reasoning. So you're stuck between a "genius" who refuses to stop bringing up outside trivia and a "simplaton" who follows the rules but can't handle a difficult task.
It sounds like we are in a bit of an arms race between the capabilities of these models and our ability to control them. If I'm a CTO today, and I'm listening to this, I'm probably thinking: "Okay, so my internal AI tool is basically a well-read liar. How do I fix this?"
You start by admitting that you can't "fix" it technically—you can only mitigate it procedurally. One of the best strategies is what we call "Negative RAG." Most people only give the model the info they want it to use. But you should also give it a "blacklist" of common misconceptions or outdated info that you know is in its training data. You tell it: "You might think the price is forty dollars because that was the old rate, but specifically ignore that; it is now fifty."
That’s clever. It’s like giving the student a list of "common mistakes to avoid on the test." But that requires a human to know what the model is likely to get wrong. It’s a lot of manual work.
It is. Another approach is "Strict Grounding" settings at the API level. Some providers are starting to offer modes where the model is literally penalized—mathematically—if it uses tokens that don't have a high probability of appearing in the retrieved context. It’s basically like tightening a leash. The tighter you pull, the more accurate the facts, but the more "robotic" and stunted the writing becomes.
I've seen that. The AI starts sounding like a broken record. "As per document A, the rate is X. Document A states that Y is true. Please refer to Document A for Z." It loses that "assistant" feel and just becomes a glorified search interface. Which, honestly, for a lot of corporate use cases, is probably what we actually want. We don't need the AI to be our friend; we need it to be a reliable filing cabinet.
But the "friend" part is what sells the software! "Talk to your data" sounds a lot sexier than "Perform a semantic search on your database and return a rigid string of text." This brings up the "Verification Gap." When a model generates a response, it’s just a string of probabilities. It doesn't actually "know" it’s citing a document. It’s just predicting that the next word should be "Source" followed by a number.
That’s the most dangerous part. The citation itself can be a hallucination. The model can write a perfectly accurate paragraph and then append a citation to a document that has nothing to do with that paragraph, simply because it knows that "professional answers have citations."
I saw a study on this recently. In some RAG setups, up to thirty percent of the citations were "hallucinated" in the sense that the text they pointed to didn't actually support the claim being made. The model was just "vibing" the citations. This is why for compliance, you need a "Hard Link" system. You don't let the model write the citation. You have the system identify which chunks of text were used and then manually append the metadata after the model is done talking.
So, we're essentially taking the "creative" part of the AI's job away bit by bit. We’re saying: "You can arrange the words, but you can't pick the facts, and you definitely can't pick the sources." At what point does it stop being an LLM and just start being a very expensive template filler?
That’s the philosophical question of twenty twenty-six, isn't it? But from a risk management perspective, that’s the goal. You want to move from "Generative AI" to "Verifiable AI." If you're in a regulated industry, you should be looking at "Attestation." Can the system provide a mathematical proof that this output is derived solely from these inputs? We aren't there yet with Transformers, but there is a lot of research into "Circuit Breaking" within the neural network—literally identifying the pathways that lead to "outside knowledge" and suppressing them in real-time.
"Circuit breaking" sounds very sci-fi. Like we're giving the AI a lobotomy every time it tries to remember something from its childhood on the internet.
It’s a bit like that. But until that tech matures, the burden is on the "Human-in-the-loop." This is the takeaway for anyone building these tools. You cannot trust the "guardrails" provided by the model makers. They are marketing terms, not physical constants. You need a governance framework.
But how do you actually implement that without hiring a thousand people to check every response? If the goal of AI is efficiency, having a human fact-check every sentence feels like we're just moving the work around.
You use "Confidence Thresholding." You don't have a human check everything. You have the AI self-assess, then have a smaller model assess the AI, and if they disagree, then it goes to a human. Think of it like a smoke detector. It’s not a fireman, but it tells you when you need to call one. If the model's internal probability for a certain fact is low compared to the source document's probability, that's a red flag.
Okay, let’s get practical. If I'm running an AI project at a big firm, what are my three "must-haves" to avoid these risks?
First, you need a dedicated "Grounding Evaluator." This is a separate process—not the model itself—that scores every response for "faithfulness" to the source. If the score is below, say, point nine-five, the response never reaches the user. It gets flagged for human review or the system says "I cannot verify this answer."
"I cannot verify this answer" is such a better response than a confident lie. It should be the default for any enterprise tool.
Second, you need "Source Transparency." Don't just show a citation; show the actual snippet of text the AI used, highlighted in the original document. This forces the human user to be the final arbiter. If the snippet doesn't match what the AI said, the human catches it. It’s about building a "Culture of Skepticism."
And the third?
"Audit Logging." You need to save not just the prompt and the answer, but the specific "context window" that was fed to the model for that specific query. If a regulator comes knocking six months later asking why your AI gave bad advice, you need to be able to recreate exactly what the AI "saw" at that moment. Was it a retrieval failure—the right document wasn't found—or was it a generation failure—the document was there, but the AI ignored it?
That distinction is huge for liability. If the document wasn't in the window, that’s a search problem. If the document was there and the AI lied anyway, that’s a model problem. Those are two very different engineering fixes.
And very different legal arguments. If it’s a search problem, you can argue it was a technical glitch. If it’s a model problem where the AI "overrode" company policy with its own "opinions," you might be looking at a much harder time proving you had control over the system.
It’s funny, we spent years trying to make these things "smarter" and more "human-like," and now companies are spending billions trying to make them "dumber" and more "robotic" so they can actually use them without going bankrupt from lawsuits.
It’s the cycle of technology. Innovation, then regulation, then refinement. We’re in the "refinement" phase where we're realizing that "knowing everything" is actually a liability in a professional setting. You don't want a doctor who "knows everything" they read on a forum; you want a doctor who knows the current, proven medical consensus.
This actually reminds me of an episode we did a while back—Episode seventeen hundred—where we talked about whether LLMs can learn continuously without forgetting. The "forgetting" part is usually seen as a flaw, but in the context of Daniel’s prompt, "forgetting" your pre-training data might actually be a feature. If we could give an AI "targeted amnesia" so it forgets everything except how to read and write, we’d solve this problem overnight.
Targeted amnesia is the holy grail. But until then, we’re stuck with these "noisy" models. And the noise is getting louder as the models get more powerful. A model like Gemini three Flash or GPT-five is going to have even "stronger" internal weights. The "gravitational pull" of its training data will be even harder to escape.
So the problem actually gets worse the better the AI gets. That is a depressing thought for the compliance team. "Congratulations, we just upgraded to the most intelligent model ever made! Also, it’s now ten times more likely to argue with our internal manuals because it thinks it knows better."
That is the reality. The more "reasoning" capability you have, the more the model will engage in what I call "Creative Inference." It won't just report the facts; it will connect the dots. And if those dots lead outside your approved corpus, you’re in the danger zone.
But Herman, isn't that connection of dots why we want AI in the first place? If I just wanted a direct quote, I'd use Ctrl+F. I want the AI to tell me why the regulatory filing from 1998 matters for my 2026 acquisition. If I strip away its ability to use its broader knowledge, don't I lose the "intelligence" part of the equation?
You've hit the nail on the head. That is the fundamental tension. We want the AI to be smart enough to understand the meaning of our documents, but we want it to be "stupid" enough to pretend nothing else exists. It's a cognitive dissonance we're forcing onto the software. The more you constrain it to the "facts" in the document, the less it can provide "insight" based on its training.
So it's a sliding scale. On one end, you have a search engine that never lies but never thinks. On the other, you have a brilliant philosopher who hallucinates half of what they say. Most businesses are desperately trying to find the middle ground, but the "middle" keeps shifting as the models update.
And those updates happen without warning. One day your RAG system is ninety-eight percent accurate. The next day, the model provider pushes a "safety update" or a "reasoning enhancement," and suddenly your model is more "creative" and starts overriding your documents again. This is why version pinning is so critical. You can't just point your enterprise app at the "latest" model and hope for the best.
This ties back to the "Verification Gap" we mentioned. We are moving toward a world where the AI provides the "draft" and a secondary system provides the "proof." I think we'll see a lot more "multi-agent" architectures where one AI agent is the "writer," one is the "librarian" who finds the books, and one is the "judge" who decides if the writer actually followed the books.
That is exactly where the industry is heading. Moving away from a "Monolithic" model approach where one brain does everything, toward a "Governance Stack." You have your LLM at the center, but it’s surrounded by layers of security, retrieval, and verification. It’s like a nuclear reactor. The LLM is the core—it’s powerful and dangerous—and the RAG pipeline and guardrails are the containment vessel and the cooling rods.
I love the nuclear analogy, even if I said we should avoid them. It works here because if the "cooling rods"—the guardrails—fail, you have a "hallucination meltdown." And instead of radiation, you get a lawsuit.
And just like a nuclear reactor, you can't just "turn off" the physics of the core. You can only manage it. The "physics" of an LLM is the transformer architecture and its pre-trained weights. You can't "un-train" it on the fly. You can only shield the output.
So, to answer Daniel’s question directly: Is it really possible to constrain a model in this fashion? The answer is a resounding "No, not perfectly." It’s an illusion created by clever engineering and a lot of "please be a good boy" prompting.
It is a "probabilistic constraint." You can make it ninety-nine percent likely that the model stays in the lines, but that one percent is always there, lurking. And for a company doing a million queries a month, that one percent means ten thousand "rogue" responses. That is a lot of risk.
So, what is the "Corn and Herman" advice for the people out there building these internal tools? Don't stop building them, but stop trusting them. Use them as "productivity accelerators" for experts who can spot the lies, not as "automated replacements" for processes that require perfect accuracy.
Precisely. If your AI is talking to a customer, it needs a much tighter leash than if it’s talking to an internal analyst. And you should always—always—have a "Grounding Score" visible. If the AI isn't sure its answer is in the documents, it should be forced to say so. We need to reward "I don't know" as much as we reward a good answer.
I'd love a "confidence meter" on every AI response. Like a little speedometer that goes into the red when the AI starts "vibing" too much. "Warning: Corn, I'm ninety percent sure I'm making this part up based on a blog post I read in twenty twenty-two."
That would be the most honest AI on the market. But until then, we have to build our own meters. The compliance and risk angle isn't just a hurdle; it’s the foundation of the next generation of AI development. We’ve had the "wild west" of generative AI. Now we’re entering the era of "Responsible Retrieval."
"Responsible Retrieval." Sounds like a boring textbook, but it’s going to be the most important textbook of the next five years. We've gone deep enough into the containment vessel for one day, I think. Before we melt down.
I think you're right. It’s a fascinating technical challenge because it’s not just about code; it’s about the fundamental nature of how these digital "brains" store and retrieve information. We’re essentially trying to teach a god-like entity how to ignore its own memory. Good luck with that.
We'll see how it goes. Thanks as always to our producer Hilbert Flumingtop for keeping us on the rails. And a big thanks to Modal for providing the GPU credits that power this show—they make the heavy lifting of these deep dives possible.
This has been My Weird Prompts. If you are enjoying the show, a quick review on your podcast app really helps us reach new listeners who are also trying to figure out why their AI is lying to them.
You can find us at myweirdprompts dot com for the full archive and all the ways to subscribe. We'll be back next time with whatever weirdness Daniel sends our way.
Until then, keep an eye on your citations.
Stay skeptical, everybody. Goodbye.
Bye.
