Fakhreddine JADIB
AI and Data Science Engineer, RAG, MCP and LLM Automation
New to Contra
Fakhreddine is ready for their next project!
If your RAG system is hallucinating, don't blame the LLM. Blame your retrieval pipeline. 🔍
This visual perfectly captures the most common point of failure in Retrieval-Augmented Generation (RAG) applications. The frustrating part? The correct information already exists in your...
The Architectural Leap: Why AI Agents Need MCP Over Traditional APIs 🔌
The shift from standard web applications to autonomous AI systems requires a fundamental change in how we handle connectivity. This diagram perfectly illustrates the architectural leap from traditional APIs...
Why Reranking Matters in RAG: Fast Retrieval ≠ Precise Relevance 🎯
If you are building Retrieval-Augmented Generation (RAG) applications, you've probably realized that just pulling the top-K results from a vector database isn't always enough to get a great answer.
This visual...
From Generative to Agentic: Building Autonomous AI Systems 🚀
The leap from generative AI to Agentic AI is the difference between a system that simply generates text and a system that autonomously executes tasks. This architecture breakdown perfectly illustrates what it takes to...
Most RAG systems fail in production for one reason: they're still running Classic RAG.
Classic RAG works beautifully in a demo.
One document store, one similarity search, one LLM call.
Then real users show up with messy queries, ambiguous intent, and edge cases your test set...
