Golden-set regression testing for LLM and extraction pipelines: scores the pipeline against 25 la...

Golden-set regression testing for LLM and extraction pipelines: scores the pipeline against 25 la...Golden-set regression testing for LLM and extraction pipelines: scores the pipeline against 25 la...

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Jigon Yoo

• Jul 28

Golden-set regression testing for LLM and extraction pipelines: scores the pipeline against 25 labeled rows, uses LLM-as-judge for fields where exact match is unfair (the judge itself is checked against human labels), and fails CI when quality regresses. It caught a real bug during development: an amount parser grabbed a store number instead of the dollar amount - field accuracy read 48%; after the fix, 100%, and the harness proves it instead of me claiming it. Offline, no API keys. Public code: github.com/jigonyoo/eval-harness

Data Engineering Python

Sumit Joshi

pro

• Jul 30

Guess 216 isn't enough to crack the top 1% Aiming for 250!!!! Lesssgo

Contra Web Development frontenddeveloper

Samuel O

pro

• Jul 30

wow that's a lot keep pushing 👍

An .

• Jul 30

TallyTrace — verify, then export.TallyTrace is the invoice tool I kept wishing existed: one that treats uncertainty as a workflow, not a footnote. It links every extracted value to visible source evidence, routes ambiguous fields into a focused review queue, detects risky duplicates, and previews a new-file-only spreadsheet export before anything leaves the workspace.I built this six-screen Flowstep prototype around one principle: automation should make financial work easier to verify, not harder to question. The signature interaction connects a selected field directly to its location on the source invoice, while the final run receipt makes every exported row traceable.Public Flowstep prototype:https://app.flowstep.ai/file?activeFileId=18aeb4f5-8f74-4621-8ac7-9c388cc1702927-second walkthrough and public social post:https://x.com/TobiUqun/status/2082662128687661283