Towards Data Science
One Vendor, Four Spellings: How Deterministic Stages Beat Similarity Scores
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Towards Data Science
Jun 9
10 Common RAG Mistakes We Keep Seeing in Production
Enterprise Document Intelligence [Vol. 1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fixes The post 10 Common RAG Mistakes We Keep Seeing in Production appeared first on Towards Data Science .
Towards Data Science
Jun 8
Increase Recommendation Systems’ Precision with LLMs, Using Python
This is how LLMs are used today to increase precision in recommendation systems The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science .
Towards Data Science
Aug 31
Why RAG Complexity Should Be Earned
The article outlines a framework for constructing Retrieval-Augmented Generation (RAG) pipelines that progressively add complexity as needed to address observed failure modes. It begins with basic lexical and hybrid search techniques, then incorporates reranking and agentic information‑seeking strategies to improve performance. The approach emphasizes that more sophisticated components should only be introduced when simpler methods prove insufficient.
Towards Data Science
Aug 20
Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One
The article explains that enterprise document intelligence can be categorized into three distinct corpus types, each requiring a specific architecture. It outlines how to determine the shape of a document collection through three key questions. The piece also discusses the costs associated with building a system for the incorrect corpus type.
Towards Data Science
Jul 6
Stop Ranking Agent Configs by Average Score
Best-worst comparisons, MaxDiff-style judging, and Plackett-Luce utility scores give agent teams a cleaner way to decide which configs to ship, prune, and route toward next. The post Stop Ranking Agent Configs by Average Score appeared first on Towards Data Science .
Towards Data Science
Jun 3
I Spent May Evaluating Different Engines for OCR
Testing fourteen engines on ninety-three human documents The post I Spent May Evaluating Different Engines for OCR appeared first on Towards Data Science .