RAG Explained: Why Your LLM Has Amnesia
Автор: NeuralCompass
Загружено: 2026-01-08
Просмотров: 14
Large Language Models are powerful — but they have a fundamental limitation: they don’t remember anything outside their training data.
In this video, I explain Retrieval-Augmented Generation (RAG) — the system design pattern that gives LLMs access to external knowledge at runtime and turns them into production-ready AI systems.
Drawing from real-world experience building ML and LLM systems at scale, this talk covers:
Why LLMs hallucinate and why prompts alone don’t fix it
What RAG is (and what it is not)
Chunking strategies and why they matter
Embeddings, vector databases, and ANN search
How retrieval context is injected into LLM prompts
The economics of RAG and why systems like Perplexity work
Why RAG is a system, not a single model or prompt
This video is aimed at ML engineers, data scientists, and practitioners looking to move from LLM demos to reliable, scalable production systems.
Image courtesy: https://www.dailydoseofds.com/
#RAG #LLM #GenerativeAI #VectorDatabases #Embeddings #MachineLearning #AIEngineering #MLOps
Доступные форматы для скачивания:
Скачать видео mp4
-
Информация по загрузке:
![Как внимание стало настолько эффективным [GQA/MLA/DSA]](https://image.4k-video.ru/id-video/Y-o545eYjXM)
