RAG on Rachid Youven Zeghlache

What is RAG? Retrieval Augmented Generation Explained

Wed, 04 Mar 2026 00:00:00 +0000

RAG Explained — How to Give Your LLM a Memory Without Retraining

You’ve probably noticed that ChatGPT doesn’t know about events from last week, or that your company’s fine-tuned model can’t answer questions about your internal documentation. Most people assume the solution is retraining the model with new data—an expensive, time-consuming process requiring GPU clusters and ML expertise.

There’s a better way. LLMs don’t actually need to “learn” new information to use it effectively. They just need access to it at the right moment. That’s the insight behind RAG (Retrieval Augmented Generation), and it’s why you’re seeing it everywhere from customer support bots to research assistants.

Agentic AI for Health

Thu, 01 Jan 2026 00:00:00 +0000

Overview

Agentic AI refers to systems where a large language model (LLM) acts not just as a question-answering endpoint, but as an autonomous agent — planning, calling tools, retrieving information, and completing multi-step tasks without constant human intervention. In healthcare, this paradigm opens the door to AI systems that can assist clinical workflows, automate research tasks, and synthesise evidence at scale.

Frameworks I work with

LangGraph

LangGraph models agent workflows as stateful directed graphs, enabling complex multi-step reasoning chains with loops, conditional branches, and human-in-the-loop checkpoints. This is particularly useful for clinical decision support pipelines that require evidence retrieval → reasoning → recommendation → validation cycles.