Data Entropy
Here’s something no one’s talking about:
👉 The entropy of your training data can predict how chaotic or focused your LLM's responses will be.
We recently cleaned a corpus with 600k+ entries.
Removing just 7% of noisy but syntactically correct text boosted output accuracy by 11%.
So the question is: Are you optimising for volume or clarity?
Tools we used:
- Whisper + custom cleanup filters
- SentenceTransformers for redundancy
- GPT for style alignment scoring
Data isn’t oil. It’s clay . How you sculpt it changes everything.
Curious: How are you handling entropy in your stack? 👇