Token-Efficient Adaptive RAG: Building a RAG System That Knows What NOT to RetrieveHow I built an adaptive Retrieval-Augmented Generation platform that reduces embedding and context tokens without blindly sacrificing retrieval quality Introduction Retrieval-Augmented Generation (RAAug 28 ยท 14 min readharshaupadrasta.hashnode.dev
Day 5 โ Chunking: The Secret to Better RAG Performance ๐งฉ๐ค๐ Advanced AI Engineering Series Artificial Intelligence systems are becoming incredibly powerful at answering questions, summarizing documents, and assisting with complex tasks. However, the effectiMar 12 ยท 7 min readharshaupadrasta.hashnode.dev
Day 4 โ How Retrieval Works Inside a RAG System๐ Advanced AI Engineering Series In the previous articles of this series, we explored two powerful building blocks behind modern AI systems: ๐ง Embeddings โ which convert text into numerical vectors Mar 11 ยท 6 min readharshaupadrasta.hashnode.dev
Day 3 โ Why Vector Databases Are Critical for AI๐ Advanced AI Engineering Series In the previous article, we explored embeddings, the hidden backbone of RAG systems. Embeddings convert text into numerical vectors that represent meaning, allowing AMar 10 ยท 5 min readharshaupadrasta.hashnode.dev
Day 2 โ The Hidden Backbone of RAG: EmbeddingsIn the previous article, we explored why modern AI systems need RAG (Retrieval Augmented Generation). RAG allows AI systems to retrieve relevant information from trusted knowledge sources before generMar 9 ยท 4 min readharshaupadrasta.hashnode.dev