Open chat
24,110 records · 18.1m tokens
chatsafety
See how your training data is organized before the model does
Review dataset clusters, compare their scale and fingerprints, and tag the slices that need curation or expansion.
Training clusters interactive preview
Training clusters
4 clusters · 58.5k records
Code & APIs
18,240 records · 12.8m tokens
pythonsql
Math & reasoning
9,420 records · 9.4m tokens
math
Multilingual
6,710 records · 5.7m tokens
trde
How the workflow fits together
Clusters turn a flat record list into an actionable map. Sort by size or recency, identify dominant pockets, and keep review tags beside each group.
Build the cluster map
Group semantically related records across the full dataset.
Inspect each pocket
Compare record counts, token volume, fingerprints, and existing tags.
Route the next action
Mark clusters for review, curation, generation, or export.
Find dominant topics
See where repeated themes may overpower the intended training mix.
Spot thin coverage
Identify small or missing clusters that need additional records.
Keep organization visible
Apply shared tags without maintaining a separate tracking sheet.
Map the real shape of your dataset
Join the waitlist for early access to training-data clustering.