You’re building a chatbot for tax advice. You’ve carefully designed a RAG pipeline, indexed the IRS documents, and picked a ‘thin’ base model. Everything looks solid. Then you ask: “Can I deduct my home office if I’m a remote employee?” The model answers confidently—and wrong. That sinking feeling in your stomach? It’s not going away. Because the truth is, you have no idea what that model actually knows, and the boundary between language and fact is an illusion.
This is the unease that haunts every serious LLM application. The industry has sold us a clean mental model: a base language model that understands grammar, plus a separate retrieval system that supplies facts. But that model is dangerously outdated. Post-training and reinforcement learning have already become the real mechanisms for injecting domain knowledge. RAG is just a brittle, leaky add-on that gives you a false sense of control.
“The boundary between language and fact is an illusion. Every LLM is a linguistic black box that happens to memorize facts.”
Look at the HN discussion. One top comment cuts through the noise: “Don’t think RAG is the most common method for now. In my knowledge now it is mostly about post-training and reinforcement learning for adding specific knowledge to base model. RAG is mostly like an add-on to a fully trained model and not so stable or inherent?” This isn’t a fringe opinion—it’s the reality of how models are actually built. The base model you think is ‘language-only’ has already been steeped in facts during pretraining. And post-training? That’s where the real knowledge injection happens, often without any transparency.
You’ve probably asked yourself: “Can I tell which LLMs have been trained on which data?” The honest answer is no. You can only test behavior. The internals are opaque. Asking about training provenance is like asking a chef to list every ingredient in the pantry—irrelevant to the meal on your plate. What matters is what comes out, not what went in. But the industry keeps pushing the fantasy that we can separate language from knowledge, that we can find a ‘clean’ base model and bolt on facts with RAG. That fantasy is dangerous.
“Post-training and reinforcement learning are the real mechanisms for injecting domain knowledge—RAG is just a brittle add-on.”
Here’s the twist: the more seamless a model seems, the harder it is to distinguish what it actually knows from what it merely generates. Language itself is built on facts. The word ‘mountain’ comes with altitude, location, and cultural baggage. There is no ‘language-only’ model. The thin base model you’re looking for doesn’t exist. And if someone claims to have one, they’re either lying or they don’t understand how their own model works.
So what do you do? Stop asking about training data. Start building rigorous output evaluation. Build a test suite that pokes at the model’s high-stakes blind spots. Use adversarial prompts that trigger hallucinations. Measure the gap between confidence and correctness. This is harder than picking a ‘safe’ base model, but it’s the only honest path.
“You can’t audit the black box. You can only test the outputs. Build for that.”
The industry is obsessed with provenance. But provenance is a red herring. The real question isn’t what was this model trained on? It’s can I trust this model’s output in my specific domain? The answer will never come from a training data disclosure. It will come from the uncomfortable, messy work of evaluation. And that’s the truth we need to face.
FAQ
Q: But can't we just use a base model and only RAG for facts?
A: No, because base models are not 'language-only.' They've already absorbed vast factual knowledge during pretraining. Post-training further injects domain-specific facts. RAG is an add-on that can be overridden by the model's internal knowledge. You can't prevent the model from using what it already 'knows.'
Q: What should I do instead of asking about training provenance?
A: Build a domain-specific evaluation suite. Test your model on edge cases, adversarial inputs, and high-stakes queries. Measure hallucination rates and confidence calibration. Accept that the model is a black box and focus on behavioral validation. The only reliable signal is output quality.
Q: Isn't this just a rehash of the 'garbage in, garbage out' problem?
A: Not exactly. GIGO assumes you can control inputs. Here, the problem is that you don't know what inputs (training data) went in. Even with perfect RAG, the model's internal knowledge can contradict or override retrieved facts. The opacity is a deeper issue than data quality—it's a fundamental epistemological limit.