Towardsdatascience

Enterprise Document Intelligence [Vol.1 #7A] - Stop searching strings. Filter line_df and toc_df. Pick anchors small, expand context large The post Retrieval Is Filtering, Not Search: A Mental Model for Enterprise RAG appeared first on Towards Data Science .

If you are a programmer and you don't feel "special" anymore, you are not alone The post The Era of No-Code AI: What You Need to Know appeared first on Towards Data Science .

From installing Ollama to launching OpenCode with a local model, step by step. The post Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode appeared first on Towards Data Science .

Why one-hot encoding isn’t always the best approach, and alternative encodings The post Encoding Categorical Data for Outlier Detection appeared first on Towards Data Science .

Learn how to apply coding agents to verify work in your browser. The post How to Use Claude Code in Your Browser appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #6bis] - Ask one focused clarification, learn the default from the answer, stay silent next time The post When RAG Users Ask Vague Questions: Clarify Once, Learn the Default appeared first on Towards Data Science .

The intuition behind neural networks and why they need activation functions. The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You appeared first on Towards Data Science .

Understanding ow LLMs interact with the world around them, from returning data to taking action The post Tool Calling, Explained: How AI Agents Decide What to Do Next appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #5septies] - When a PDF prints a contents page but exposes no outline, two ways to turn it back into structure, plus the page-alignment step everyone forgets The post Reconstructing the Table of Contents a PDF Forgot to Ship, So RAG Can Scope by Section appeared first on Towards Data Science .

For years, I created date tables with DAX code whenever I didn’t have a way to create them upstream of the data flow. Now I've realised there's another way to do it. Let’s see what the alternatives are and how they compare. The post What Are the Possibilities to Build Date Tables in Self-Service Environments? appeared first on Towards Data Science .

What data teams need to build with AI to make self-healing data architecture a practical reality The post 7 Crucial Barriers Between Data Teams and Self-Healing Data Architecture appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #5sexies] - image_df tells you where every picture is. Turning the few that matter into searchable text is a separate, cost-ordered job The post Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All appeared first on Towards Data Science .

Five surfaces collapsed into one declarative layer. Here's the full story of Materialized Lake Views in Microsoft Fabric - from syntax to the new GA capabilities The post Materialized Lake Views in Microsoft Fabric: When Your Medallion Fits in a SELECT Statement appeared first on Towards Data Science .

A technical overview and some benchmarks The post Python 3.14 and its New JIT Compiler appeared first on Towards Data Science .

Why Custom Inference in DeepStream? The post Building a Custom GStreamer Plugin for NVIDIA DeepStream appeared first on Towards Data Science .

What I thought was a scheduling problem turned out to be a portability problem first The post I Tried to Schedule My ETL Pipeline. Here’s What I Didn’t Expect. appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #5quinquies] - Same 1974 scanned PDF, two engines. EasyOCR recovers text. Docling recovers text + sections + figures. The structural gap makes one output usable downstream and the other one a flat string. The post Parse Scanned PDFs for RAG with EasyOCR: Free OCR Gives You Words, Not a Document appeared first on Towards Data Science .

The PCIe transfer latency is silently bottlenecking your agentic inference. Here is how building a custom device-resident vector search kernel bypasses the CPU to unlock deterministic microsecond tail latencies. The post GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step Would Stop Bouncing Off the GPU appeared first on Towards Data Science .

Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .

Learn about the upsides and downsides of Claude Fable 5 The post How Powerful is Claude Fable (Mythos) 5 for Coding? appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .

A hands-on guide to setting up image similarity search in Milvus, and why visual replication isn't always enough. The post The Power and Pitfalls of Vector-Based Image Search appeared first on Towards Data Science .

How unit economics should set your classification cutoff, and why they rarely do. The post Your Churn Threshold Is a Pricing Decision appeared first on Towards Data Science .

Why production-level AI optimization modeling agent needs reproducibility and portability, and how IR helps achieve them The post The Secret to Reproducible and Portable Optimization: ORPilot’s Intermediate Representation (IR) appeared first on Towards Data Science .

Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python. The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #6b] - The five field families the parser reads straight from the user’s question, with the code that fills each one The post What the Question Parser Extracts from a User String: Keywords, Scope, Shape, Decomposition, Clarification appeared first on Towards Data Science .

Budgets for AI tokens can’t be infinite, no matter how much hyperscalers wish they were The post Drilling Into AI’s Financial Sustainability appeared first on Towards Data Science .

Tired of your monthly API bill? Follow this tested guide to set up a high-performance local LLM on your Mac Mini without the headaches. The post Run a Local LLM with OpenClaw on Your Mac Mini appeared first on Towards Data Science .

LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that classifies failures, adapts payloads across model tiers, preserves execution state, and maintains schema integrity during provider swaps. The post LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #6a] - Why a user question deserves the same parsing as the document, and how it splits into a retrieval brief and a generation brief before either runs The post RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation appeared first on Towards Data Science .

Increase productivity with your LLMs The post How to Effectively Align with Claude Code appeared first on Towards Data Science .

A detailed look at MCP that turned my scattered tool definitions into a stable, discoverable server The post The Protocol That Cleaned Up Our Agent Architecture appeared first on Towards Data Science .

A single model hands you a single answer and no sense of how much it hinges on the dozens of choices buried inside it. The post I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions. appeared first on Towards Data Science .

How local optimization in last‑mile delivery can quietly break the system The post The System Always Knows: Why Local Efficiency and System Performance Are Not the Same Problem appeared first on Towards Data Science .

Without these, Claude will be confidently wrong. The post 4 Lines You Should Include in Your Claude Skill appeared first on Towards Data Science .

Enterprise Document Intelligence [Vol.1 #5quater] - The other parsers read the words on a page. A vision model also reads the pictures The post Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG appeared first on Towards Data Science .

A systems-level deep dive into the hidden microarchitectural costs of Kubernetes GPU time-slicing, and what it actually costs to co-locate Agentic AI workloads. The post GPU Time-Slicing for Concurrent LLM Agents on Kubernetes appeared first on Towards Data Science .

Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG entirely. The post Larger Context Windows Don’t Fix RAG — So I Built a System That Does appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page bill, nothing leaves the building The post Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload appeared first on Towards Data Science.

Let's practice data science thinking through a probability problem The post Solving the 3Blue1Brown String Probability Problem (Without AI) appeared first on Towards Data Science.