# Dataset curation Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/dataset-curation
> Markdown URL: https://aitinkerers.org/technologies/dataset-curation.md
> Technology record last updated: 2026-04-01T22:28:29Z
> Generated: 2026-09-22T03:54:19Z

Dataset curation is the systematic process of cleaning, labeling, and filtering raw data to build high-performance AI models.

Modern AI performance depends more on data quality than model architecture. Curation involves removing duplicates (deduplication), fixing label errors, and balancing class distributions to prevent bias. Platforms like Hugging Face and tools like Cleanlab allow engineers to audit millions of rows (such as the 5-trillion-token FineWeb dataset) to ensure training sets are diverse and accurate. By filtering out low-quality noise and PII, teams reduce compute costs and improve downstream accuracy metrics like MMLU scores.

- Official technology site: https://huggingface.co/docs/datasets/index
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [How to Argue With a Language Model (And Win)](https://belgium.aitinkerers.org/talks/rsvp_RV-s6edPsfU)

You've built an AI agent, it mostly works, and now you're stuck in a loop of tweaking prompts and hoping for the best. Sound familiar? In this talk we'll move past vibes-based development and into structured experimentation. We'll cover how to set up A/B tests for your agents, build and curate datasets from captured interactions or static data, and wire up evals that actually tell you whether your changes made things better or worse.

- Event context: AI Tinkerers Antwerp Meetup - April 1 (no joke) — 2026-04-01 — Belgium
- Public talk page: https://belgium.aitinkerers.org/talks/rsvp_RV-s6edPsfU

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