Uploaded July 2025 | Updated September 2026, 2 weeks ago
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: colab.research.google.com/drive/1sMVAivJ1pn-7iNnByEPF4aUlQPCt_s39?usp=sharing
huggingface.co/naver/provence-reranker-debertav3-v1
huggingface.co/blog/nadiinchi/provence
arxiv.org/pdf/2501.16214
github.com/PromtEngineer/localGPT
Website: engineerprompt.ai
RAG Beyond Basics Course:
prompt-s-site.thinkific.com/courses/rag
Let's Connect:
🦾 Discord: discord.com/invite/t4eYQRUcXB
☕ Buy me a Coffee: ko-fi.com/promptengineering
|🔴 Patreon: patreon.com/PromptEngineering
💼Consulting: calendly.com/engineerprompt/consulting-call
📧 Business Contact: engineerprompt@gmail.com
Become Member: tinyurl.com/y5h28s6h
💻 Pre-configured localGPT VM: bit.ly/localGPT (use Code: PromptEngineering for 50% off).
Signup for Newsletter, localgpt:
https://tally.so/r/3y9bb0
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
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: colab.research.google.com/drive/1sMVAivJ1pn-7iNnByEPF4aUlQPCt_s39?usp=sharing
huggingface.co/naver/provence-reranker-debertav3-v1
huggingface.co/blog/nadiinchi/provence
arxiv.org/pdf/2501.16214
github.com/PromtEngineer/localGPT
Website: engineerprompt.ai
RAG Beyond Basics Course:
prompt-s-site.thinkific.com/courses/rag
Let's Connect:
🦾 Discord: discord.com/invite/t4eYQRUcXB
☕ Buy me a Coffee: ko-fi.com/promptengineering
|🔴 Patreon: patreon.com/PromptEngineering
💼Consulting: calendly.com/engineerprompt/consulting-call
📧 Business Contact: engineerprompt@gmail.com
Become Member: tinyurl.com/y5h28s6h
💻 Pre-configured localGPT VM: bit.ly/localGPT (use Code: PromptEngineering for 50% off).
Signup for Newsletter, localgpt:
https://tally.so/r/3y9bb0
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
