The Steering Stack
Fine-tuning steers behavior; it does not teach knowledge. The five-layer mental model that anchors the entire course — and the one judgment that prevents the cardinal error of treating fine-tuning as knowledge injection.
Fine-tuning steers behavior; it does not teach knowledge. The model already knows how to do X — it saw X during pretraining. Fine-tuning redirects the model's probability mass so it does X reliably, in your format, under your conditions.
You can swap any layer above the base without touching the one below. That is why LoRA adapters are swappable, why abliteration works without retraining, why you quantize after training, why the harness is model-agnostic. The stack is modular by design.
If the base model, with a perfect prompt, could already produce the target behavior, that's steering. If it could not, no amount of fine-tuning will reliably get you there — you need a different base, continued pretraining, or (usually) RAG.
The model steers; the harness bounds. This is the complement, not the contradiction, of Course 1's thesis. The two courses describe the same system from opposite ends.