LLM Data Pipelines at Scale
How OpenAI and Anthropic process trillions of tokens for GPT and Claude
Why These Case Studies Matter
The quality of an LLM is largely determined by its training data. OpenAI and Anthropic have built sophisticated data pipelines that process trillions of tokens, filtering for quality, safety, and diversity at unprecedented scale.
These case studies reveal the complete data curation stack: web crawling, deduplication, quality filtering, toxicity detection, and human feedback collection. You'll learn techniques that apply whether you're training a 7B parameter model or fine-tuning an existing LLM.
Learning Path: After reading these case studies, build your own LLM data pipeline with the LLM Data Pipeline Project, then follow the step-by-step walkthrough.
Note on Metrics: These case studies are based on publicly available information from engineering blogs, conference talks, and open-source documentation. While we've verified core architectural patterns and technologies, some specific numbers (especially cost figures and exact scale metrics) are estimates for educational purposes. Where possible, we've updated unverified claims to reflect documented information or general ranges.
Featured Case Studies
Deep dives into OpenAI's GPT and Anthropic's Claude data pipelines
OpenAI
Case Study #1
The Problem
Training GPT-3/4 required processing the entire internet - trillions of tokens from web pages, books, code repositories, and conversations. Needed to filter low-quality data, remove duplicates, detect toxicity, and ensure diverse representation while respecting copyright and privacy.
Scale
Anthropic
Case Study #2
The Problem
Training Claude required curating high-quality, safe, and diverse training data while implementing Constitutional AI principles. Needed data pipeline to prioritize helpfulness, harmlessness, and honesty (HHH) while filtering harmful content at scale.
Scale
Continue Learning
Build Your Own Data Pipeline
Practice with the LLM Data Pipeline project - crawl, filter, and tokenize training data
Troubleshooting Guide
Common LLM pipeline errors - from rate limiting to deduplication at scale
Step-by-Step Walkthrough
Complete walkthrough for building the LLM data pipeline from scratch
More Case Studies
Explore how companies use RAG, Agentic AI, LLM Evaluation, and more