5 Simple RAG Fixes That Will Transform Your AI Apps
Автор: ZeroEntropy
Загружено: 2025-12-04
Просмотров: 331
Building an RAG system that works in real production environments requires far more than embeddings. In this video, we break down the 5 most common retrieval failure modes engineers hit.
zerank-2 benchmarks → https://www.zeroentropy.dev/articles/...
Architecture Docs → https://docs.zeroentropy.dev/architec...
00:00 – Why most RAG systems fail in production
00:20 – Failure Mode #1
01:20 – How RRF fixes hybrid retrieval
01:49 – Failure Mode #2
02:37 – Why embeddings alone can’t order results correctly
04:04 – How re-rankers reorder results for maximum relevance
04:49 – Benchmarks: zerank vs other embedding models
05:36 – Failure Mode #3
06:32 – Why most re-rankers have unstable scoring
08:10 – Why LLMs make terrible re-rankers
08:54 – Failure Mode #4
09:42 – How to chunk, contextualize, and tag data correctly
10:34 – Failure Mode #5
12:20 – Gemini vs zerank-2 list-wise benchmarks
12:55 – ZeroEntropy hybrid search architecture
#zerank2 #rag #zeroentorpy #ragarchitecture
Доступные форматы для скачивания:
Скачать видео mp4
-
Информация по загрузке:
