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[[{“value”:”I explain why re-ranking isn’t enough for RAG and show how sentence-level pruning strips out noisy tokens and cuts hallucinations. You’ll see the token savings, accuracy boost, and a quick setup you can drop into any retrieval pipeline. Try this swap and watch your RAG answers get sharper.
Notebook: https://colab.research.google.com/drive/1sMVAivJ1pn-7iNnByEPF4aUlQPCt_s39?usp=sharing
https://huggingface.co/naver/provence-reranker-debertav3-v1
https://huggingface.co/blog/nadiinchi/provence
https://arxiv.org/pdf/2501.16214
https://github.com/PromtEngineer/localGPT
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00:00 Introduction to Reducing Hallucination in RAG Systems
01:07 Challenges with Traditional RAG Systems
01:25 Practical Example: DeepSeek Paper
03:21 Introducing the Pruning Phase
04:36 Provence Model for Context Pruning
06:37 Performance and Availability
07:08 Demo and Practical Use
08:51 Licensing and Future Prospects”}]] Read More Prompt Engineering
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