Releases·Tool Release
POMA AI says cheap ingestion fixes beat tuning embeddings: PrimeCut cut benchmark context from 1.45M to 340K tokens
A POMA AI team member shares six document-ingestion lessons from a year of RAG debugging, arguing that structure-aware chunking moves retrieval quality more than expensive model upgrades. The post doubles as a launch pitch for their PrimeCut API, which they benchmark against Unstructured.io on Databricks' OfficeQA questions over U.S. Treasury Bulletins.
1 engagement1 sourceMon, Sep 14, 2026, 01:31 PM
AI Overview
- POMA AI reports five near-free ingestion fixes moved RAG numbers where embedding upgrades, rerankers, and bigger context windows failed for a year
- Two-column PDFs commonly get columns interleaved by parsers, silently embedding unrelated sentences together without failing any evaluation
- POMA AI advises detecting text layers before OCR and extracting complex tables as HTML or XML since Markdown cannot represent row or column spans
- POMA AI says fixed-size chunking like RecursiveCharacterTextSplitter (500, 100) treats document structure as token counting and recommends structure-aware splitting
- On 20 Databricks OfficeQA questions over 14 Treasury Bulletins (~2,150 pages), PrimeCut held 100% evidence recall; Unstructured.io's by_title needed 1.48M tokens
- PrimeCut combines parsing, multi-OCR detection, and structure-aware chunking for 50+ file types via managed SaaS or on-premises container
Related
Social·Community
RAG failures often trace back to document parsing issues
A developer asks how often RAG failures actually stem from document parsing problems rather than chunking, embeddings, or retrieval. The post suggests that broken table structure, heading hierarchy, or reading order corrupts input before retrieval, making parsing a common hidden bottleneck.
A developer asks how often RAG failures actually stem from document parsing problems rather than chunking, embeddings, or retrieval. The post suggests that broken table structure, heading hierarchy, or reading order corrupts input before retrieval, making parsing a common hidden bottleneck.
11 engagement1 sourceredditWed, Aug 26, 2026, 01:57 PM
Social·Community
Practitioners report RAG extraction failures on production documents
A practitioner observes that RAG pipelines that work well on clean demo PDFs fail on real-world documents like scanned forms and multi-column statements, with extraction layers silently producing wrong data. Tables dissolve or misalign, causing retrieval to return incorrect answers without errors.
A practitioner observes that RAG pipelines that work well on clean demo PDFs fail on real-world documents like scanned forms and multi-column statements, with extraction layers silently producing wrong data. Tables dissolve or misalign, causing retrieval to return incorrect answers without errors.
22 engagement1 sourceredditMon, Aug 3, 2026, 05:17 PM
Social·Community
Reddit guide to building a production RAG agent: parsing, chunking, hybrid retrieval, evals, and observability
A Reddit practitioner guide argues LLM choice is rarely what breaks production RAG systems; parsing, chunking, retrieval, evals, and observability matter more. It recommends specific tools for each stage and building evals before tuning. The thread is a discussion prompt inviting others to add their own practices.
A Reddit practitioner guide argues LLM choice is rarely what breaks production RAG systems; parsing, chunking, retrieval, evals, and observability matter more. It recommends specific tools for each stage and building evals before tuning. The thread is a discussion prompt inviting others to add their own practices.
5 engagement1 sourceredditTue, Sep 22, 2026, 10:18 AM
Social·Community
From Ingestion to Agents: How AI Teams Build on Document Intelligence — Adit Abraham, Reducto
In a talk, Reducto CEO Adit Abraham argues document parsing is now the bottleneck for agents, since bad inputs compound across multi-step workflows. He describes Reducto's agentic OCR approach — token-level corrections rather than page regeneration — and reports that parsed structure lifts frontier models above the best model's ~30% score on a PDF decision benchmark while cutting reasoning tokens.
In a talk, Reducto CEO Adit Abraham argues document parsing is now the bottleneck for agents, since bad inputs compound across multi-step workflows. He describes Reducto's agentic OCR approach — token-level corrections rather than page regeneration — and reports that parsed structure lifts frontier models above the best model's ~30% score on a PDF decision benchmark while cutting reasoning tokens.
50 engagement1 sourceyoutubeWed, Sep 23, 2026, 02:30 PM
Releases·Tool Release
Reddit user builds layout-aware pdfplumber PDF parser for cleaner RAG chunking
A developer shared a Python layout-aware PDF parser built on pdfplumber and FastAPI that reconstructs multi-column layouts and tables into Markdown before RAG chunking. The approach uses visual grid coordinates rather than blind left-to-right text extraction, and the author is soliciting comparisons against vision/OCR models for text-based PDFs.
A developer shared a Python layout-aware PDF parser built on pdfplumber and FastAPI that reconstructs multi-column layouts and tables into Markdown before RAG chunking. The approach uses visual grid coordinates rather than blind left-to-right text extraction, and the author is soliciting comparisons against vision/OCR models for text-based PDFs.
1 engagement1 sourceredditThu, Sep 17, 2026, 09:40 AM