DocETL tackles the notebook-to-production gap — with a visual playground, academic rigor, and some painful scaling costs
7 min readJust now
–
I’ve spent the last few years watching LLM application development settle into a frustrating pattern. You start with a Jupyter notebook — some langchain or just raw OpenAI calls—experimenting with prompts on a handful of documents. It works okay. Then you try to scale it to the actual dataset: 10,000 PDFs, messy formatting, edge cases that break your regex. Suddenly you’re writing brittle preprocessing code, retry logic, batching boilerplate, and before you know it, that elegant prompt engineering experiment has become a maintenance nightmare.
The transition from “experiment” to “production pipeline” for document processing is genuin…
DocETL tackles the notebook-to-production gap — with a visual playground, academic rigor, and some painful scaling costs
7 min readJust now
–
I’ve spent the last few years watching LLM application development settle into a frustrating pattern. You start with a Jupyter notebook — some langchain or just raw OpenAI calls—experimenting with prompts on a handful of documents. It works okay. Then you try to scale it to the actual dataset: 10,000 PDFs, messy formatting, edge cases that break your regex. Suddenly you’re writing brittle preprocessing code, retry logic, batching boilerplate, and before you know it, that elegant prompt engineering experiment has become a maintenance nightmare.
The transition from “experiment” to “production pipeline” for document processing is genuinely painful. Most teams either stay stuck in notebook hell or over-engineer with Airflow DAGs that take 20 minutes to iterate on.
DocETL, a project out of UC Berkeley’s EPIC lab, attacks this specific gap. It’s not just another LLM wrapper — it’s a declarative pipeline system with a visual playground, designed specifically for the messy reality of large-scale document processing.