Start Here🔗
I'm Wassim, ML Engineer at iAdvize in Paris. I build LLM & RAG systems at production scale evaluation, observability, retrieval, agentic architectures. This page is a curated entry point into my work and the resources I find most valuable.
Key Themes in My Work🔗
RAG Systems🔗
- A Small Trick to Avoid Citation Hallucination Why incremental document IDs invite hallucination, and how to fix it
LLM Optimization & Evaluation🔗
Posts coming soon topics I'm actively writing about: prompt engineering patterns, evaluation frameworks, cost optimization at scale.
Engineering Philosophy🔗
- Why You Should Avoid AI Frameworks They abstract the wrong layer. Own your context, own your prompts.
Agentic Systems🔗
Posts coming soon migrating from static pipelines to agentic architectures, when agents help and when they don't.
Must-Read External Resources🔗
Resources I keep coming back to and recommend to anyone working on production AI.
Guides & Reference
- What We Learned from a Year of Building with LLMs Eugene Yan et al. Practical lessons from shipping LLM products
- Applied LLMs Community-driven resource on building with LLMs
- Anthropic's Prompt Engineering Guide Best practices for prompting
- LMSYS Chatbot Arena Live LLM benchmarking by human preference
Papers
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks The original RAG paper (Lewis et al.)
- Lost in the Middle How LLMs struggle with long contexts
- Chain-of-Thought Prompting Wei et al., foundational prompting technique
Tools
- LangSmith LLM observability and evaluation
- Ragas RAG evaluation framework
- vLLM High-performance LLM serving
Projects & Competitions🔗
- Kaggle NFL Helmets Detection Silver Medal (Top 5%). Computer vision competition detecting helmets in NFL footage
- This blog Built with Material for MkDocs, deployed on GitHub Pages. Source code
Math🔗
My personal passion. I use math to build intuition about how things work from probability theory to optimization. See Math for Fun.