Inside the Subspace Where Spurious Correlations Are Born
Why small samples can produce large correlations by chance, and why large does not always mean meaningful
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A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers
Enterprise Document Intelligence [Vol.1 #9A] - Same paper, same question as Article 1. One upgraded contract per brick: document parsing, question parsing, retrieval, generation
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Increase the effectiveness of your coding agents through end-to-end testing.
The post How to Run End-to-End Tests with Claude Code appeared first on Towards Data Science.
Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop
Enterprise Document Intelligence [Vol.1 #8C] - Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedback
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AI Agents Explained: What Is a ReAct Loop and How Does It Work?
How agents reason, act, and observe their way to a final answer, one step at a time
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Long Context vs. Short Context Model: When Does a Long Context Model Win?
Balancing context capability against cost, speed, and data
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The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation
Enterprise Document Intelligence [Vol.1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG
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Tokenminning: How to Get More from Your Chatbot for Less
Tokenmaxxing is out. Real patterns for reducing costs without sacrificing AI effectiveness
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The Untaught Lessons of RAG Question Parsing: Structure Before You Search
Enterprise Document Intelligence [Vol.1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook
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The next leakage problem is not only temporal. It is spatial, structural, and coverage-related. AI-generated illustration created with DALL·E
The post Why Powerful ML Is Deceptively Easy — Part 2 appeared first on Towards Data Science.
In the age of AI, standing out here means a lot more than ever. Here are three tips to walk into your next interview with confidence.
The post Surviving the Data Science Behavioral Interview appeared first on Towards Data Science.
Stop Choosing Between Local and Cloud LLMs: A Field Guide to Hybrid Patterns
A hands-on walkthrough of a hybrid local-cloud workflow using Gemma 4 and GPT-5.4, with reasoning and structured outputs
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How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context.
The post How Far Can Classical NLP Go? Fro...
Prompt Engineering Fails Quietly — Prompt Regression Is Why
Small prompt changes can silently break critical behavior in production. This article introduces a practical framework to detect hidden regressions before users notice.
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Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation
Why memorizing for the exam doesn't mean you understand the subject
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Beyond the Straight Line: Choosing Between OLS, Interaction Terms, and Tweedie Regression
Whether you should stick to a classic Ordinary Least Squares regression, introduce interaction terms, or pivot to a Tweedie distribution depends entirely on how your data handles the messy reality of zeros and extreme outliers.
The post Beyond the Straight Line: Choosing Between OLS, Interaction Ter...
One Month Into Learning Data Engineering in Public: Here’s What I Didn’t Write About
A reflection on the first month of learning data engineering in public, and what actually kept me going.
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How to Build a Credit Scoring Grid From a Logistic Regression Model
Turning model coefficients into a 0–1000 score, with risk classes and stability checks
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