This is the most important thing people still don’t get about building AI--whether you're training a model or designing an agentic system: data quality is everything.
The difference between good data and bad data is the difference between your AI understanding a task… or getting lost in the noise.
Here’s another mistake I see constantly:
People assume their AI already understands the basics of a domain.
It doesn’t. And often... it can’t.
You have to write your data like you're teaching a clever but confused intern.
Best case: it knows a little.
Worst case: it’s hallucinating with confidence.
An LLM won’t always tell you when it’s confused.
It’s your job to be the adult in the room, make sure the data is clean, clear, and operational.
Fine-tuning? Sure, that’s useful when you need to reshape a thought process.
But most of the time?
You don’t need smarter weights. You need better scaffolding.
Start with cleaner data.
Less noise.
Explicit structure.
Clear separation of signals and metadata.