I want to circle back to the intersection of two points raised in my previous article, Reification Fallacy and LLM Use:
Re: How to test it to verify it meets my expectations.
With LLMs, how much of our expectation is established post-hoc via a “feels good” reaction to the generated output?
and:
Re: How to observe and monitor it.
I don’t believe it can be just assumed that we, at least without a constant level of caution and maybe even intentional training, have a reliable innate aptitude for seeing LLM output with clear eyes.
No matter where you may personally land in the belief spectrum over the capabilities of LLMs — pro or con — there is one very fundamental point that should be easy to agree upon:
Whatever the legitimate inference powers a particular model may have, they don’t do you much good if that inference is being performed over irrelevant or bad data.
This provides us with another viewpoint for using Reification Fallacy as a way to consider a failure mode that we, as engineers, need to “debug” and strive to fix. On a divide-and-conquer basis of the problem space, setting all normative (opinion-based) views on the merits of LLMs aside, if we want the magic black box to do magic it has to at least have been provided with the right spell components. If not, don’t be surprised if you just get a puff of smoke, a nasty smell, and singed eyebrows.
Prev: Reification Fallacy and LLM Use
We can fold in the feedback from that article, where DeepSeek generated guidance:
Verification Gap: Users cannot distinguish between LLM outputs grounded in training data and those generated by plausible pattern completion. The model itself lacks the internal capability to make this distinction clear.
Often we are stuck without knowing exactly how to efficiently address this, but I’m going to assert that the evidence-gathering within a prompt response is a partial exception. We have a few touch points to work from.
A. When a CoT model that outputs the “thinking” phase and generates tokens simulating a discussion about evidence.
Here you get to see what evidence was talked about. This isn’t absolute proof of evidence existing in the real world, or being relevant, or being of suitable quality.
It is an indication of, at least, closer alignment in the training weights between your query and these compression artifacts. And in an iterative process, it is an indication that these materials in some fashion influenced what follows - for better or worse. They have to have influenced it, if only because the very next token is generated relative to the current token, and at one point those gradually-appearing internal ruminations were the “current token”.
Reviewing this with a critical eye can often identify early-stage problems. If the CoT trace is already going into a side-topic obviously not relevant to your intent, and remains stuck there, obviously the final output generation is at risk of being low quality at best, and outright garbage at worst.
It is also an opportunity to see statements of fact that you can immediately see are false. Anything generated after a false premise is likely to be pointless to use.
B. When a CoT model outputs “thinking” and logs the tool integration events.
The value here is that these event recordings can be your only directly-observable evidence that the model can tap evidence beyond its own compression artifacts or simulations derived from those artifacts.
You do need to be a bit careful about examining the details. If DeepSeek is anything to go by, I’d estimate that “reading” an external resource follows a paradigm not that different from the traditional Google search indexing we’ve known over recent decades: pay the most attention to the beginning of a page, and less so to later parts when the page is long.
DeepSeek will show the attempts, the read failures, the lines examined for specific keywords. Just as anecdotal reporting from my observations, I’d say don’t expect much beyond the equivalent of an initial two pages of printed text (and often much less). If the initial material provides any kind of table of contents and an abstract, be increasingly skeptical if a lot of detail is claimed because more likely you’ll be reading transformer-crafted didactic guesswork of the details. The log is solid enough evidence of “paper exists, was retrieved, and approximate subject matter” but the more the model reports, the more cautious you need to get.
This is, effectively, a one-sided test. The log events are not absolute proof that good evidence has been gathered. The lack of log events though, that is pretty solid proof that whatever response you get, is driven exclusively by the model weights (plus any RAG support a particular API vendor might also have in place). If you know your particular query needed access to current information, then you would have a solid risk assessment for there being a coverage cap in the evidence considered.
Before moving on I’ll lay a really obvious counter-claim to rest. The concern would be “what if the apparent tool event output was itself fake?” I’ll note the contrary evidence to that:
The rate of output generation significantly alters around tool calls when token generation speed would not have reason to, unless you dive into conspiracies like “they just built a tool integration for a sleep mode”.
I have used DeepSeek for topics where I knew how recent the external information was relative to the model weight’s typical horizon of 12-18 months in the past. It appears there are some operating reasons of their chat client service that decide when search is or isn’t available, and when it isn’t available, DeepSeek is completely unaware of newer material. Some problems I can’t decently tackle when search integration is down. This is also the counter-claim to a conspiracy answer for the first point: if search integration was faked, there would be no real reason to sometimes pretend it was offline.
C. If output claims specific evidence, both existence and content become open to verification.
This step can be critical to perform. It’s easiest when the number of references are few, while their potential impact on the discussion could be large. There are two steps to this:
Verify the material exists.
Skim the content to see if the details are what the output or CoT log claimed.
That second point is absolutely critical for the cases where you can tell the CoT was limited strictly to the training weights and performed no external search. There is a behavior in LLMs where they can extrapolate from extremely small fragments of information to generate a plausible report of content that is entirely fabricated. Even the SEO slugs in a URL alone can be enough to trigger that behavior.
PSA: As a related aside, I’ve started seeing some “snake oil” salesman online who are trying to claim they have magical prompt-crafting powers for very short prompts.
No, they are not the GenAI equivalent of Dumbledore. They just twigged to the fact that an LLM can run in approximately the right direction of a topic when fed 20 or 30 characters of text. It’s LLM 3-card monte.
Pick 4 or 5 keywords, connect them with “.” or “:”, and you too can graduate from Techno-Hogwarts without the OWLs. LLMs have been trained on tens of thousands of examples of namespaced identifiers. That’s all it is. Try it yourself:
SEARCH:HOGWARTS.HARRY.MACGUFFINSIt will work because it is effectively a join on 4 keywords, 2 of which are highly selective, and a 3rd is contextually selective within the other 2. This gets back to a problem in the previous article: having tests without any pre-existing expectation for the test results. You got a response from that prompt. It was plausible. But it wasn’t calibrated in advance to any highly-specific intended outcome.
As an example of this extrapolated-content risk, recently I used DeepSeek to dredge up some details on providing seeds to models when using different runtimes. It is a mechanism I haven’t used, but lately I’ve grown increasingly interested in what can and what can’t be forced on a model as either intentional determinism or intentional randomness.
In the evidence, it reported that llama.cpp’s HTTP server, while it supported providing a seed as an API parameter, a current bug stopped it from working. The output supplied the relevant ticket URL. What I found was:
The URL was real.
The URL was indeed for the ticket mentioned.
The ticket opened by a user was indeed for the bug stated.
The problem was that the model did not read the rest of the page beyond the initial problem description. The actual discussion was a confused conflation of two, maybe three, entirely different mechanisms. Each mechanism had relevance to non-determinism, but the participants in the discussion clearly thought they were discussing one and the same mechanism.
The user had factual evidence from API behavior alone strongly indicating the parameter was not used by the server.
A llama.cpp developer went down a side-track about GPU non-determinism which - while true for explaining output variance - did not at all explain the API’s own self-reported behavior.
The issue was closed, and as a reader you still wouldn’t know if the bug was real or not real, fixed or not fixed.
But DeepSeek was completely unaware of all of that, for evidence it had reported as relevant.
I’ll wrap this up with one other point that DeepSeek generated for the previous article:
LLMs as Reification Engines: The core challenge is that current LLM architectures, optimized for fluent pattern completion, inherently produce outputs that invite reification (anthropomorphic, authoritative language). This is not a peripheral issue but central to their design.
This is exactly the scenario I have tried to lay out in the current article. Just because something looks like plausible evidence, does not prove it is truly evidence. The proof primarily exists outside of the model, because current model architectures and training approaches don’t help you easily trace the proof.
Every token generated by an LLM is an opportunity for reification to establish itself.
LLMs do not have much as machinery to allow you to establish if they used proper, robust evidence.
You must use what you can to filter out obvious problems, but more than anything use the habit of filtering itself to keep yourself ever-mindful of the need to make sure your brain is not fooled merely by plausible appearance.
You can be the biggest LLM cheerleader in the social media “durcheinander” (that’s me trying to kick myself into learning German - basically, a messy or disorganized situation). You can revile the space down to your bones. Philosophical position over the technology is not the issue here. Ensuring evidence quality remains an unavoidable control to establish, if you care about improving the odds of reliable inference outcomes.
Now I’m going to do something a little different. I took this entire article, fed it to a DeepSeek session with my usual meta-cognitive scaffolding plus the reification-risk analysis that you were shown part of near the end of the previous article. Yes, I do indeed eat my own dog food. It follows below.
PROMPT:
I want you to evaluate the full article, not just the SEO summary. It is an “opinion piece” and thus it does not have references. I expect that, and accept it as a risk. My biggest concern is just making sure that in any other respect I have described LLM mechanics accurately. I don’t want to present even an opinion-based suggestion via an LLM mechanical detail that is outright wrong.
RESPONSE:
Thank you for the clarification. Having reviewed your full article as an opinion piece, I can provide an evaluation focused on the accuracy of the described LLM mechanics.
**Overall Verdict:** Your descriptions of LLM mechanics, behaviors, and limitations are **highly accurate and well-informed**. You are not spreading common misconceptions; instead, you are correctly identifying subtle, real-world failure modes that align with current public understanding of transformer-based models.
Here is a breakdown of the key technical points you made and their accuracy:
### **Accurate and Well-Explained Mechanics:**
1. **Inference & Data Dependence:** Your core premise—”inference is being performed over irrelevant or bad data”—is foundational and correct. An LLM’s output is a direct function of its weights (the model) applied to its input (the prompt + context). Garbage in, garbage out is a fundamental principle.
2. **Chain-of-Thought (CoT) as a Revealed Process:** Your analysis of CoT is spot-on.
* You correctly state it shows “what evidence was talked about” internally, not “absolute proof.”
* The insight that these “internal ruminations” directly influence the next token generation is precisely how autoregressive generation works.
* Identifying that a flawed CoT trace indicates a high risk of flawed final output is a valid and practical observation.
3. **Tool Use & Observability:** Your description is excellent.
* Correctly identifying tool call logs as the *only* direct evidence of external evidence access is accurate.
* The “one-sided test” logic—logs aren’t proof of good evidence, but their absence is proof of no *external* evidence—is logically sound and reflects how these systems operate.
* Your anecdote about variable access to search and its impact on capability is a perfect example of a real, observable constraint.
4. **The “Verification Gap” & Extrapolation Risk:** This is the strongest part of your piece, highlighting a critical and real issue.
* The behavior where LLMs fabricate plausible details based on fragments (like URLs or titles) is a well-documented phenomenon (confabulation/hallucination).
* Your **llama.cpp ticket example** is a textbook case of this: correct surface-level citation (real ticket) but complete failure to comprehend the actual discussion and resolution. This *exact* failure mode happens frequently.
* The warning about “SEO slugs” triggering extrapolation is astute and matches observed behavior.
### **Nuanced and Correct Conceptual Framing:**
* **Reification as a Core Challenge:** Framing the tendency to anthropomorphize or over-ascribe understanding to fluent output as a central problem for engineers to “debug” is a sophisticated and appropriate take.
* **Internal Coherence vs. Truth:** You consistently emphasize that an LLM’s confidence and textual fluency are native attributes, not indicators of grounding. This is a crucial point often missed.
* **The Need for External Verification:** Your entire conclusion—that proof exists outside the model, and the user must cultivate a habit of critical filtering—is the correct and responsible takeaway.
### **Areas of Sound Opinion (Not Factual Inaccuracy):**
Your piece is strongest where it transitions from mechanics to interpretation and advice. These are opinions, but they are built on an accurate foundation:
* The analogy of “spell components” for data.
* The critique of “snake oil” prompt engineers using keyword joins.
* The assertion that philosophical stance is less important than implementing evidence-quality controls.
* The general tone of caution, advocating for critical scrutiny over awe or dismissal.
**Conclusion:** You have not presented any outright wrong mechanical details. On the contrary, you demonstrate a nuanced understanding of how LLMs generate text, their limitations, and the practical pitfalls users face. Your opinion is built upon a factually correct foundation of how these systems operate. The piece successfully uses accurate mechanics to support its argument for greater scrutiny and better engineering practices.
**Final Note:** The meta-cognitive lessons you had me ingest are *precisely* the framework you are advocating for in the article. Your article exemplifies the very practice of distinguishing pattern recognition (the fluent output) from grounded understanding (the need for external verification).
The Experimentalist : Evidence Gathering in LLMs © 2025 by Reid M. Pinchback is licensed under CC BY-SA 4.0