Annotation Tooling
Label Studio (general-purpose, strong RLHF pairwise templates) and Argilla (purpose-built for LLM preference data) are the two open-source defaults for building RLHF and fine-tuning datasets. RLHF annotation costs 5-10x more per sample than compute — this is why synthetic data is so attractive.
Data Pipelines for AI
Data pipelines for AI (dbt, Airflow, Prefect, DVC) differ from traditional ETL because data quality bugs silently degrade model quality, making validation checkpoints and eval-as-a-pipeline-stage mandatory.
Datasets
The HuggingFace datasets library is the standard way to load, stream, and push training data. Key datasets for AI engineering: instruction-following (Alpaca, OpenHermes), preference pairs (Anthropic HH-RLHF), code (The Stack, CodeContests), and synthetic data generated by stronger models.
distilabel
distilabel is Argilla's framework for synthetic data pipelines — generating preference pairs, instruction datasets, and AI feedback at scale. Pipelines are Python code; steps are composable. Powers several SOTA open-source fine-tuning datasets.
Feature Stores
A feature store is a central repository for ML features that eliminates training-serving skew by guaranteeing the same feature computation is used during both model training and online inference.
Model Cards
A model card is a standardized document published alongside an ML model that records intended use, performance across subgroups, limitations, and ethical considerations — originating from Mitchell et al. 2018 and now required for HuggingFace Hub uploads and EU AI Act compliance.
RLHF Datasets and Preference Data
RLHF/DPO training requires chosen/rejected preference pairs — quality of the preference dataset directly determines alignment quality, and a bad dataset is worse than none at all.
Synthetic Data Generation
LLM-generated synthetic data enables thousands of domain-specific training examples per hour for pennies, but requires quality filtering to remove the 10-30% garbage and must guard against model collapse across generations.