Programming Tutorials

  Moving a Retrieval-Augmented Generation (RAG) prototype from a Jupyter notebook to a production environment is where the real engineering begins. In a controlled environment with 50 documents, a basic   top_k   vector search works perfectly. In production with 500,000 chunks, two critical issues emerge: retrieval latency spikes, and the LLM begins to "hallucinate" answers because the retrieved context—while mathematically "close" in vector space—is semantically irrelevant to the specific user query. This post details how to implement L2 normalization for retrieval speed and a Re-ranking pipeline to eliminate context noise. The Root Cause: High-Dimensional Noise and Index Traversal To fix RAG, you must understand why it fails at scale. Latency & The Dot Product Shortcut:  Most vector databases calculate distance using Cosine Similarity. However, calculating the magnitude of vectors during search is computationally expensive. Many production systems default to D...