Everybody always jumps to AI training as the catch all solution for business problems. This is wrong. Consultants and vendors don't usually push back on training even when they know it won't work, because training is expensive and time consuming.
This creates a perverse incentive to go along with it, no matter how bad the idea is.
Nine out of ten times, the reason why LLM's fail at a process is because you haven't broken the steps down effectively enough to be understood by an eight-year-old that requires permission for everything.
Key places your process can break down.
- Noise (are you throwing vectors at your agents because you can... or because you need to?). How clean is the instruction data?
- Ambiguous instructions - Are your steps laid out clearly?
- Unlabeled tool responses - Labeling is the difference between successes that are successful, and successes that become failures.
Before thinking about training... think about your data and instruction pipeline, and save yourself $30,000 in compute.
Training can improve the machine’s reasoning on specific problems.
It can even cover up minor defects in your process (like the duct tape effect you see in most RAG fine-tuning). But that’s fragile, and prone to new categories of hallucination.
Fine-tuning is not the answer for busted information pipelines — at least not if your goal is to build solutions that survive years, not months, in deployment.