Fine-tuning is far more reliable at teaching a model style, format and behavior than at reliably injecting new factual knowledge, because the training signal from a modest instruction dataset is small relative to the vast knowledge already encoded during pretraining. A model can appear to learn new facts during fine-tuning, but that knowledge is often shallow, poorly generalized and prone to being forgotten or contradicted at inference time, especially when the facts appear only a handful of times in the dataset. Reliable factual knowledge injection generally requires either continued pretraining on a large volume of domain text, so the facts are seen thousands of times in varied contexts, or retrieval augmented generation, which supplies facts at inference time rather than baking them into weights. Fine-tuning excels at teaching consistent tone, output structure, task-specific reasoning patterns, refusal behavior and domain vocabulary usage, which is why most production systems combine RAG for facts with fine-tuning for behavior. Teams that expect fine-tuning alone to make a model an expert on a large private knowledge base are usually disappointed. Nanobase AI designs RAG and fine-tuning together rather than treating fine-tuning as a knowledge injection shortcut.
A simple test that exposes the real answer
Rather than accepting a general claim about whether fine-tuning can teach facts, a direct test on your own data settles the question for your specific case. Take a set of facts that appear only once or twice in the training data and a separate set that appear dozens of times across varied phrasings, then query the fine-tuned model on both after training, using questions phrased differently from how the facts appeared in training. The pattern that emerges is consistent across most enterprise fine-tuning projects: facts seen rarely are recalled unreliably or not at all, while facts repeated frequently across varied contexts are recalled far more consistently.
Testing recall on rarely seen versus frequently repeated facts on your own dataset is more informative than any general rule about whether fine-tuning teaches knowledge.
Why repetition count matters more than which method is used
The underlying reason is statistical, not a limitation specific to LoRA versus full fine-tuning. A modest instruction dataset provides a comparatively small gradient signal for any single fact relative to the vast amount of knowledge already encoded during pretraining on trillions of tokens. A fact appearing once in a thousand-example fine-tuning set barely nudges the weights compared to a fact the base model saw thousands of times during pretraining, or would see thousands of times during continued pretraining on domain text. This is true whether the fine-tuning method is LoRA, QLoRA, or full fine-tuning, since the limitation is about signal strength relative to data volume, not about which parameters get updated.
The repetition count of a fact in training data, not the fine-tuning method chosen, is the primary driver of whether that fact gets reliably learned.
What reliably transfers versus what does not
| Capability | Learned reliably by fine-tuning | Why |
|---|---|---|
| Output format and structure | Yes | Consistent pattern reinforced across every example |
| Tone and writing style | Yes | Strong, consistent stylistic signal per example |
| Task-specific reasoning pattern | Yes, with enough examples | Demonstrated repeatedly across training data |
| Rare or one-off factual claims | No, unreliably at best | Weak signal relative to pretrained knowledge |
| Broad domain knowledge | Only via continued pretraining | Needs millions of tokens of repeated exposure |
Format and style sit on one side of this table for a structural reason, and knowledge sits on the other for the same reason, which is why the two need different solutions.
Building a system that uses each technique for what it is good at
Once the fact-injection test confirms the expected pattern, the practical path forward is straightforward: use retrieval augmented generation to supply facts at inference time, where they can be sourced, cited, and kept current without retraining, and use fine-tuning to teach the consistent tone, format, and reasoning pattern the model should apply to those retrieved facts. This combination is why most mature production systems pair RAG and fine-tuning rather than picking one, a framework explored further in our RAG versus fine-tuning decision guide. Teams that expect fine-tuning alone to make a model an expert on a large private knowledge base are testing the wrong tool against the wrong job.
Route facts through retrieval and route behavior through fine-tuning; asking either technique to do the other's job is the recurring mistake.
Frequently asked questions
Does continued pretraining solve the factual knowledge problem?
Largely yes, since exposing a model to millions of tokens of domain text with facts repeated across many contexts is a fundamentally different signal strength than a modest instruction dataset, though it requires far more data and compute than standard fine-tuning.
Can we just repeat important facts many times in a small dataset?
To some degree, but artificially repeating the same few facts dozens of times in a small dataset risks overfitting the model to those exact phrasings rather than genuinely internalizing the underlying fact in a generalizable way.
Is this limitation specific to smaller open-weight models?
No, the same signal-strength dynamic applies across model sizes and applies to closed frontier models as well; it is a property of how gradient-based fine-tuning interacts with a modest dataset, not a limitation of any particular model family.
How do we know if a fact "generalized" versus was memorized verbatim?
Test with the fact phrased differently from its exact training-data wording. A model that only answers correctly when the question closely matches the training phrasing has memorized rather than generalized the fact.
How Nanobase AI helps
Nanobase AI designs RAG and fine-tuning together rather than treating fine-tuning as a knowledge injection shortcut, running the fact-injection test described here as part of scoping every project so clients know upfront what a fine-tuned model will and will not reliably know. See our solutions for how we combine these techniques in practice.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.