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Towards Data Science

Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy | Towards Data Science How to Find the Optimal Coding Agent Interface | Towards Data Science Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents | Towards Data Science Where Does an AI’s Personality Actually Come From? | Towards Data Science The Real Challenge Limiting AI Models Today Redesign Work Before You Add More AI Agents Inside the Subspace Where Spurious Correlations Are Born The Threshold Is a Price, Not a Percentage Information Theory and Ensemble Models Granger Causal Networks and Indirect Feedback Measuring Structure Stability of Econometric Models A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation Identifying Microbes in Space Survival Analysis for Data Drift and ML Reliability How to Run End-to-End Tests with Claude Code Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop Stop Ranking Agent Configs by Average Score Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs PANet Paper Walkthrough: When Feature Pyramids Go Bottom-Up Setting Up Your Own Large Language Model AI Agents Explained: What Is a ReAct Loop and How Does It Work? Long Context vs. Short Context Model: When Does a Long Context Model Win?
Stop Returning Text from RAG: The Typed Answer Contract That Prevents Hallucination
Kezhan Shi · 2026-07-04 · via Towards Data Science
Enterprise Document Intelligence [Vol.1 #8A] - The schema is the contract: every field is a question the pipe…

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