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towardsdatascience.com > your-llm-can-return-perfect-json-and-still-be-wrong

Your LLM Can Return Perfect JSON and Still Be Wrong

1+ week, 6+ day ago   (1426+ words) What I learned after thinking more carefully about Structured Outputs on messy, incomplete data Three weeks after I turned on Structured Outputs for a pipeline that parsed payment confirmation messages into transaction records, I noticed that our reconciliation job started…...

Towards Data Science
towardsdatascience.com > one-document-type-a-million-files-structured-extraction-into-the-sql-table-rag-queries

One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries

2+ week, 6+ day ago   (1842+ words) Enterprise Document Intelligence [Vol.1 #14C] - One hour with two people, six to ten fields, and the two signals that separate a real column from one that will break a filter later A lot of RAG work right now goes into letting…...

Towards Data Science
towardsdatascience.com > estimating-from-no-data-deriving-a-continuous-score-from-categories-2

Estimating from No Data: Deriving a Continuous Score from Categories

3+ week, 2+ day ago   (938+ words) A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training To be able to illustrate the work, I developed a toy example, which is a non-linear but deterministic…...

Towards Data Science
towardsdatascience.com > scaling-an-integration-pipeline-without-breaking-correctness

How to Scale an Integration Pipeline Without Breaking Correctness

3+ week, 4+ day ago   (1069+ words) A production account of the throughput work behind a 16x jump — and the two guarantees it was never allowed to trade away Two guarantees sat underneath every throughput change, and every one of the optimizations later in this article is built…...