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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Zero-Shot Local Document Parsing with Gemma 4: Treating PDFs as Images
Treating PDFs as images and feeding those images to Gemma 4 dissolves the scanned-versus-digital distinction that makes every text-extraction pipeline fragile. Fix that.
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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YOLO26 Tutorial: Object Detection, Pose Estimation & More
Looking to model to implement pose estimation? I know something that can perform detection, instance segmentation, pose estimation and classification, all of that in real-time. Yes, I’m talking about the YOLO26 from ultralytics. It can aid security systems or can be fine-tuned to detect even smalle...
Large Action Models (LAMs) vs Agentic LLMs: What’s the Real Difference?
You tell your AI “Polish my email and send it.” Same sentence, three outcomes. The gap between Large Action Models (LAMs) and agentic LLMs is one of the most practically important distinctions in AI today, and also one of the least clearly explained. In this article, we cut through the confusion thr...
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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This article takes a gentle dive into the ultimate AI systems evaluation benchmark, outlining why it was created, curating diverse opinions from groups of experts in the field about it, and wrapping up with a summary of the most widely accepted verdict.
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
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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.
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The Best $20 AI Plan: ChatGPT Plus vs Claude Pro vs Gemini Pro
Three chatbots. Same price of $20 for their subscriptions. The convergence is almost funny considering how different the offerings are. The same price does not mean the same product. I paid for all three and ran the same work through each. They are not interchangeable as you’ll soon find out. Pick w...
7 Real-World Python Projects You Can Build in 2026 (With Guides)
Check out this practical list of Python projects covering AI automation, machine learning, APIs, dashboards, data analysis, and portfolio-ready apps, with guides, demos, repositories, and datasets.
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...