Secret Context Engineering Trick For RAG

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 Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Let's Connect: 🦾 Discord: https://discord.com/invite/t4eYQRUcXB ☕ Buy me a Coffee: https://ko-fi.com/promptengineering |🔴 Patreon: https://www.patreon.com/PromptEngineering 💼Consulting: https://calendly.com/engineerprompt/consulting-call 📧 Business Contact: engineerprompt@gmail.com Become Member: http://tinyurl.com/y5h28s6h 💻 Pre-configured localGPT VM: https://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