Risk-Adjusted Returns with Python (Part 1): The Treynor Ratio
Author(s): Siddharth Mahato Originally published on Towards AI. “Risk comes from not knowing what you’re doing.” — Warren Buffett Most investors chase returns. But ask any seasoned fund manager, and you’ll hear a different question:“Am I being rewarded fairly for the risks …
How REFRAG Delivers 30× Faster RAG Performance in Production
Author(s): MKWriteshere Originally published on Towards AI. Intelligent context compression reduces latency and infrastructure costs for development teams If you’ve ever built a Retrieval-Augmented Generation system, you know the pain. Your chatbot pulls 20 relevant documents, feeds them to your LLM, and …
From Manual EDA to AI-Powered Agents: A Hands-On Experiment with LangChain
Author(s): Sarah Lea Originally published on Towards AI. Can an agent take over repetitive EDA tasks? A quick LangChain experiment. Exploratory data analysis (EDA) is a standard step before we train models or make predictions. Own visualization — Illustrations from unDraw.coThe article …
Detailed Guide to Quantisation Methods for LLMs
Author(s): Parth Chokhra Originally published on Towards AI. A Visual Step-by-Step Guide to Popular Quantisation Techniques Quantisation is the process of reducing the precision of numbers used in a model; for example, storing weights in 8-bit integers instead of 16- or 32-bit …
5 ML Mistakes That Scream “Student” (And How to Fix Them) 🚀
Author(s): MahendraMedapati Originally published on Towards AI. From campus to career-ready: Transform your machine learning projects with these industry insights As a student diving deep into machine learning, you’ve probably built some cool projects, aced those assignments, and maybe even topped a …
Introduction to RAG: Basics to Mastery. 5-Advanced RAG: Fast Retrieval (ANN) and Reranking
Author(s): Taha Azizi Originally published on Towards AI. Part 5 of the mini-series introduction to RAG Introduction In the earlier articles, we built RAG pipelines that worked great for small datasets.But what happens when your knowledge base grows to millions or billions …
Multi-Agent Workflows & The Right Data Foundation for The Next Evolution of Enterprise AI
Author(s): Tobi Beck Originally published on Towards AI. Single AI agents are hitting enterprise limits, but multi-agent workflows unlock 3–5x better performance through specialized collaboration — if you solve the data foundation challenge first. Source: Image by the author While most enterprises …
How I Used My Gmail Inbox to Uncover AI Agent Trends with Python
Author(s): Saleh Alkhalifa Originally published on Towards AI. A step-by-step journey from Gmail exports to uncovering the rise of AI Agents AI Agents and “agentic AI” have exploded in popularity in recent years, with milestones like AutoGPT, LangChain, and Anthropic’s MCP shaping …
I Built a Local Clinical AI Agent from Scratch — Here’s How
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. How I wired GPT-OSS with custom tools to make clinical data actually usable. In my last experiment, I ran OpenAI’s new local model on my laptop and it extracted JSON from clinical notes …
Applied Mathematics: The Hidden Engine Powering Tomorrow’s Breakthroughs
Author(s): Saif Ali Kheraj Originally published on Towards AI. How applied mathematicians solve real-world optimization problems in industry In the world of operations research and applied mathematics, few algorithms are as elegant and powerful as the simplex method for solving linear programming …
Introduction to RAG: Basics to Mastery. 4-RAG with MCP- The Future of Dynamic Context Retrieval
Author(s): Taha Azizi Originally published on Towards AI. Part 4 of the mini-series introduction to RAG Introduction So far in this series, we’ve explored: Basic RAG with local semantic search. Hybrid RAG combining keyword + semantic search. Agentic RAG with multi-step reasoning …
Designing Data Pipeline Architectures for Machine Learning Models
Author(s): Kuriko Iwai Originally published on Towards AI. A practical guide to transforming raw data into actionable predictions A data pipeline architecture serves as the strategic blueprint for transforming raw data into actionable predictions. Photo by Ayush Kumar on UnsplashThe article discusses …
5 Prompting Techniques That Changed My Life as an AI Engineer (and Everyday AI User)
Author(s): Taha Azizi Originally published on Towards AI. How I stopped guessing prompts and started engineering them — for work, code, and everyday life. Personal + professional playbook from someone who uses LLMs for groceries, side projects, and production systems at large …
Predicting Premier League match wins using Bayesian Modelling
Author(s): Adil Said Originally published on Towards AI. Source: Photo by Samuel Regan-Asante on Unsplash Introduction Football is the greatest and most-watched sport in the world, and according to Fifa, it has around 5 billion fans worldwide. The Premier League is arguably …
LLMs Don’t Need Search Engines: They Can Search Their Own Brains
Author(s): MKWriteshere Originally published on Towards AI. SSRL Framework Proves AI Models Already Contain the Knowledge They Keep Looking Up We’ve been training AI to ask Google for answers when we should have been teaching it to remember what it already knows. …
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