Structure-guided NER optimization for enterprise GraphRAG systems
The post Proxy-Pointer RAG: Eliminating Wasteful Entity & Relations Extraction in Knowledge Graphs appeared first on Towards Data Science.
RAG Is Burning Money — I Built a Cost Control Layer to Fix It
Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without s...
Five Questions About Chronos-2, the Time Series Foundation Model
Part 1: A practitioner's walkthrough of univariate, multivariate, covariate-informed, and cold-start forecasting.
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EmoNet: Speaker-Aware Transformers for Emotion Recognition — and What I’d Build Differently in 2026
A retrospective on my MS thesis, the leaderboard it placed on, and the LLM shift that has reshaped the field since.
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The Infrastructure Behind Making Local LLM Agents Actually Useful
Lessons from building a fast, reliable scientific agent with local open-weight models, vLLM, and long-context infrastructure
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DiffuJudge-AV: A Diffusion-Inspired Framework for Calibrated AV Video Evaluation
A diffusion-inspired framework for stress-testing and denoising LLM-as-a-Judge pipelines, applied to safety-critical driving video.
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Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings
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Most AI Agents Fail in Production Because They’re Built Backwards
Good models don't save bad architecture, and most teams learn that the hard way.
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How I turned 100 messy pdfs into structured insights by building a deterministic loop around agents
The post Stop Using LLMs Like Giant Problem Solvers appeared first on Towards Data Science.
The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment
How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment.
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I Built My First ETL Pipeline as a Complete Beginner. Here’s How.
A beginner's honest walkthrough of Extract, Transform, Load using the GitHub API
The post I Built My First ETL Pipeline as a Complete Beginner. Here’s How. appeared first on Towards Data Science.
From TF-IDF to Transformers: Implementing Four Generations of Semantic Search
How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search systems step by step using Python.
The post From TF-IDF to Transformers: Implementing Four Generations of Semantic Search...
Introducing the Agent Toolkit for Amazon Web Services
It’s like having your own personal expert AWS solutions architect and data engineer rolled into one.
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From Prototype to Profit: Solving the Agentic Token-Burn Problem
Engineer token-efficient, self-adapting workflows for production
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Hybrid AI: Combining Deterministic Analytics with LLM Reasoning
How AI architecture prevents plausible but wrong analytics
The post Hybrid AI: Combining Deterministic Analytics with LLM Reasoning appeared first on Towards Data Science.
Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale
For AI engineers who want to understand every step, not just call the library
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The Hidden Bottleneck in Quantum Machine Learning: Getting Data into a Quantum Computer
Quantum Machine Learning promises access to exponentially large representational spaces, but before any computation can happen, classical data must first be embedded into quantum systems. This article explores one of the most overlooked bottlenecks in QML: getting data into a quantum computer effici...
Prompt Engineering Isn’t Enough — I Built a Control Layer That Works in Production
Most LLM failures in production aren’t random — they’re predictable.
I kept hitting broken JSON, silent failures, and outages that froze my entire app. Prompt engineering didn’t fix it.
So I built a control layer above the model — and took structured output reliability from 0% to 100% without changi...
Optimizing AI Agent Planning with Operations Research and Data Science
AI agents can quickly become expensive without a clear strategy for planning, skill coverage, and budgets. This article shows how to use operations research and data science to optimize AI agent cost and resource allocation. You will learn how to frame common agent problems—skill coverage, project a...