Why Most RAG Systems Fail in Production and the Simple Fix That Improves Accuracy Fast
Author(s): Divy Yadav Originally published on Towards AI. Source: By the author You spent two weeks building a RAG application. It retrieves documents. It generates answers. You tested it with a few questions. It looked good. Then you put it in production …
How to Pick the Best OCR Model for Text, Table & Graph Parsing — Using OCR Arena
Author(s): Days of Developer Originally published on Towards AI. How to Pick the Best OCR Model for Text, Table & Graph Parsing — Using OCR Arena Optical Character Recognition (OCR) has become a key enabler for digitising documents, automating data flows, and …
Data Imputation in Machine Learning: A Practical, No-Nonsense Guide (ML Chapter -2, Module-2)
Author(s): Sayan Chowdhury Originally published on Towards AI. Missing data shows up everywhere: surveys, logs, sensors, medical records, finance datasets, you name it. And if you feed missing values directly into most ML models, they’ll crash or behave unpredictably. That’s why data …
Stopping AI Hallucinations: A New Data Science Playbook
Author(s): The Braveheart writerd Originally published on Towards AI. Stopping AI Hallucinations: A New Data Science Playbook Ask a Vision-Language Model (VLM) how many Matryoshka dolls are in an image, and it might confidently lie to you. dataBot — “AI explores data …
5 Secrets to Mastering RL Agents and Rewards Fast
Author(s): Vikram Lingam Originally published on Towards AI. Everything you need to know about reinforcement learning and why it matters Reinforcement learning (RL) has transformed how machines tackle complex tasks, from self-driving cars navigating traffic to robots assembling parts in factories. In …
The Orthogonality Paradox: We’ve Been Wrong About Space
Author(s): DrSwarnenduAI Originally published on Towards AI. The trap we don’t know we’re in You think you understand space. The article discusses the implications of dimensionality in understanding space and mathematics, particularly how our intuitive grasp of lower dimensions doesn’t hold true …
Inside the Cognitive Substrate: How Next-Generation AI Systems Are Evolving Beyond Statistical Learning
Author(s): Zain Ahmad Originally published on Towards AI. Sharing my journey through the next frontier of AI development and cognition I still remember the first time I really paused and thought about what AI could do beyond just predicting the next word …
Agentic AI Project: Build a Multi-Agent System With LangGraph
Author(s): Alpha Iterations Originally published on Towards AI. This is an end-to-end project on building a multi-agent insurance support system using Agentic AI [LangGraph and OpenAI API]. [Code Included]. Non members read here for free. Multi Agent System Architecture [Image by Author]The …
How to Handle an Imbalanced Dataset In Machine Learning Using SMOTE
Author(s): Tanesh balodi Originally published on Towards AI. How to Handle an Imbalanced Dataset In Machine Learning Using SMOTE All that people ask for in a machine learning model is the accuracy of the model; this accuracy is sometimes nothing but a …
RAG: The Backbone of Modern AI Applications — What, Why, How, and the Latest Advancements
Author(s): Yuval Mehta Originally published on Towards AI. Photo by Kevin Ku on Unsplash Artificial Intelligence has reached a stage where models can generate fluent, human-like text, but not always factually correct or context-aware. This is where Retrieval-Augmented Generation (RAG) comes into …
The Tools That Automate 90% of Your Work While You Get a Good Night’s Sleep
Author(s): Shreyansh Jain Originally published on Towards AI. A practical breakdown of how deep agents like Gemini, ChatGPT, and Claude plan, read, and research for you — even overnight. To understand why tools like Gemini Deep Research feel so powerful, we need …
Scaling Laws: How to Allocate Compute for Training Language Models
Author(s): M Originally published on Towards AI. From Chinchilla’s 20:1 rule to SmolLM3’s 3,700:1 ratio: how inference economics rewrote the training playbook Training a language model is expensive. Really expensive. A single training run for a 70 billion parameter model can cost …
Cookiecutter Data Science: A Standardized, Flexible Approach for Modern Data Projects
Author(s): Abinaya Subramaniam Originally published on Towards AI. In the ever-evolving world of data science, one of the biggest challenges isn’t the algorithms or tools, it’s project organization. If you are working solo or collaborating with a team, maintaining a clean, reproducible, …
Transformer in Action —Optimizing Self-Attention with Attention Approximation
Author(s): Kuriko Iwai Originally published on Towards AI. Discover self-attention mechanisms and attention approximation techniques with practical examples The Transformer architecture, introduced in the “Attention Is All You Need” paper, has revolutionized Natural Language Processing (NLP). Photo by NordWood Themes on UnsplashThis …
Data Lakes in Enterprises
Author(s): Flora Nanda Originally published on Towards AI. Data is now widely seen as the new “gold standard” in the AI revolution. In the context of AI, data is the critical foundation and enabler for everything from model training to real-time decision-making …
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