RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
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RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
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About this listen
Paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)
Let’s stop guessing. Let’s search.
LLMs hallucinate. They don't know your private data.
In this episode, we dive into RAG, the architecture that changed how modern AI systems handle knowledge.
Instead of relying solely on parametric memory (weights), models can now retrieve external documents to produce factual answers.
We break down the 2020 seminal paper and explain:
🔹 Why Large Language Models hallucinate.
🔹 The "Retriever-Generator" architecture.
🔹 Why retrieval has become the backbone of Enterprise AI.
Clear intuition, real examples, and practical insight.
🎧 Listen now to master the tech behind ChatPDF and Search.
As we close the year, tell us what you think in the Q&A!