I Tested Claude on 30+ Drug Interactions. The Failure Wasn’t Accuracy
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. What modern medical AI gets right — and the safety problem it still can’t solve I spent a week trying to trick Claude into giving dangerous medical advice. I tested 30+ drug combinations, …
Building Self-Correcting RAG Systems
Author(s): Kushal Banda Originally published on Towards AI. Self-correcting RAG systems Standard RAG pipelines have a fatal flaw: they retrieve once and hope for the best. When the retrieved documents don’t match the user’s intent, the system generates confident nonsense. No feedback …
Fine-Tuning Large Language Models (LLMs) Without Catastrophic Forgetting
Author(s): Sachchida Nand Singh Originally published on Towards AI. Introduction Fine-tuning large language models (LLMs) is no longer optional — it is the standard way to adapt foundation models to domains such as healthcare, finance, legal text, customer support, or internal enterprise …
LLM & AI Agent Applications with LangChain and LangGraph — Part 28: Multi-Agent Discussion Panel (Researcher, Expert, Critic, Moderator)
Author(s): Michalzarnecki Originally published on Towards AI. Hi. In this part I’ll present a multi-agent application. It will be a discussion panel with four roles: Researcher — has access to a search tool and brings facts and sources into the conversation. Expert …
LLM & AI Agent Applications with LangChain and LangGraph — Part 27: The Publisher Agent (News → Summary → Article → Critic → Improve)
Author(s): Michalzarnecki Originally published on Towards AI. Hi. In this part I’ll run and demonstrate a publisher agent — a system that autonomously aggregates news from the web, summarizes the most important points, and then generates an article based on that material. …
LLM & AI Agent Applications with LangChain and LangGraph — Part 25: AI Agents architectures(and How to Organize Them)
Author(s): Michalzarnecki Originally published on Towards AI. Hi! In this article I’ll demonstrate different types of AI agents. This topic is useful because it helps you understand how many ways “intelligent agents” can behave and be organized — from simple reactive systems …
mHC: Rethinking the Neural Highway
Author(s): Revanth Madamala Originally published on Towards AI. If you’ve been following the evolution of Deep Learning, you know that for the last decade, we’ve been obsessed with Residual Connections (ResNets). They are the “highways” of a neural network — the bypass …
Setting Up TensorFlow with GPU (CUDA): A Step-by-Step Installation Guide
Author(s): Muaaz Originally published on Towards AI. If you are writing Deep Learning code on a machine with a GPU, TensorFlow will default to running on the CPU. This happens because TensorFlow does not automatically select the best hardware. To use the …
Why Intelligent Systems Fail Quietly
Author(s): Mind the Machine Originally published on Towards AI. Hallucination, confidence, and the hidden cost of punishment-driven optimization This article continues the line of inquiry started in Mind the Machine, which examined how modern discussions about AI often overlook deeper structural properties …
LLM & AI Agent Applications with LangChain and LangGraph — Part 26: RAG AI Agent in LangGraph
Author(s): Michalzarnecki Originally published on Towards AI. Hi. So far in this series we’ve built a basic graph, and then a graph with an LLM and a conditional loop. We also covered different types of AI agents. Now we’ll do a practical …
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