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Most RAG (Retrieval Augmented Generation) systems fail because of weak retrieval, poor chunking, and lack of ranking strategies. In this video, I break down ๐ฎ๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐ฅ๐๐ ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐พ๐๐ฒ๐ used in production systems to ๐ถ๐บ๐ฝ๐ฟ๐ผ๐๐ฒ ๐ฟ๐ฒ๐ฐ๐ฎ๐น๐น, ๐ฝ๐ฟ๐ฒ๐ฐ๐ถ๐๐ถ๐ผ๐ป, ๐ฎ๐ป๐ฑ ๐ฟ๐ฒ๐ฑ๐๐ฐ๐ฒ ๐๐ผ๐ธ๐ฒ๐ป ๐ฐ๐ผ๐๐. ๐ฌ๐ผ๐ ๐๐ถ๐น๐น ๐น๐ฒ๐ฎ๐ฟ๐ป: ย ย 1. Why basic RAG pipelines fail ย ย 2. The principle of โQuality In = Quality Outโ ย ย 3. Semantic chunking vs naive chunking ย ย 4. Using metadata for better filtering ย ย 5. Generating synthetic questions to boost recall ย ย 6. ๐ช๐ต๐ ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐ก๐ข๐ง ๐ฎ๐น๐๐ฎ๐๐ ๐ฟ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐ฑ ย ย 7. When relational databases outperform vector stores ย ย 8. ๐๐ฒ๐ป๐๐ฒ ๐๐ ๐๐ฝ๐ฎ๐ฟ๐๐ฒ ๐ฟ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น explained ย ย 9. ๐๐๐ฏ๐ฟ๐ถ๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต (๐๐ ๐ฎ๐ฑ + ๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ) ย ย 10. Re-ranking strategies for better precision ย ย 11. How to control high recall and reduce token cost If you're building LLM-based systems, enterprise search, or AI copilots, this video will help you design production-ready RAG architectures. ๐ ๐๐๐น๐น ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ๐ฑ ๐ฐ๐ผ๐๐ฟ๐๐ฒ ๐ https://quantumroot.in/courses/generative-ai-large-language-models-with-langchain-and-huggingface ๐ ๐ข๐๐ต๐ฒ๐ฟ ๐๐ฒ๐ป๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฏ ๐๐ฒ๐ป๐๐ ๐ณ๐ผ๐ฟ ๐๐๐๐ถ๐ป๐ฒ๐๐ https://quantumroot.in/courses/generative-ai-made-simple-for-everyone-in-business ๐ญ ๐๐ป๐ฑ๐๐๐๐ฟ๐ถ๐ฎ๐น ๐๐ฒ๐ป๐๐ ๐๐ฒ๐ฎ๐ฑ๐ฒ๐ฟ๐๐ต๐ถ๐ฝ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ https://quantumroot.in/courses/industrial-genai-leadership-program ๐ ๐๐ฒ๐ป๐๐ ๐ณ๐ผ๐ฟ ๐๐ฎ๐ ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐๐ถ๐ป๐ด & ๐๐ฃ๐ ๐ฅ๐ฒ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป https://quantumroot.in/courses/generative-ai-for-b2b-marketing-cpl-reduction ๐งโ๐ผ ๐๐ฒ๐ป๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ & ๐ฅ๐ฒ๐ฐ๐ฟ๐๐ถ๐๐ถ๐ป๐ด ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ผ๐ป https://quantumroot.in/courses/generative-ai-for-hr-recruiting-automation