Скачать или смотреть 5 Re-Ranking Hacks to Boost RAG Accuracy by 40%

In this video, I show you how I built a comprehensive Re-ranking Playground that demonstrates 5 different document re-ranking techniques to solve one of the biggest problems in RAG systems - the “Lost in the Middle” problem!

What You’ll Learn:
✅ The “Lost in the Middle” Problem - Why LLMs struggle with information in long contexts
✅ 5 Re-ranking Techniques - Cross-Encoder, LLM-based, Cohere API, Hybrid, Learning to Rank
✅ Building with Streamlit & FastAPI - Modern web app with production-ready API
✅ Real-time Evaluation - NDCG, MRR, MAP metrics with interactive Plotly visualizations
✅ Code Implementation - Complete walkthrough of each technique✅ Performance Comparison - Speed vs Accuracy trade-offs explained

Key Features I Built:
🔥 Interactive Playground - Test your own queries or use preset examples🔥 5 Re-ranking Methods - From neural networks to traditional ML
🔥 Real-time Metrics - See performance comparisons instantly
🔥 Backend API - FastAPI backend with full documentation
🔥 Educational Content - Code examples and case studies
🔥 Modern UI - Sleek Streamlit interface with session persistence

Why Re-ranking Matters:
Stanford research shows that LLMs lose up to 40% performance when relevant information is buried in the middle of long contexts. I demonstrate how re-ranking solves this by optimally positioning the most relevant documents!

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GitHub Repo: https://github.com/AIAnytime/Re-ranki...

Lost in Middle Paper: https://cs.stanford.edu/~nfliu/papers...

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