Welcome to    by Kuriko IWAI.

In Retrieval-Augmented Generation (RAG), your model’s output is strictly capped by the quality of the retrieved context.

This technical deep-dive explores the transition from arbitrary text slicing to semantic optimization. We evaluate the trade-offs between fixed-token splits and advanced hierarchical structures, providing Python implementation patterns to ensure your vector database delivers coherent, context-rich information for complex queries.

Building a RAG prototype is easy; ensuring it doesn't hallucinate in production is the real engineering challenge.

This article dissects the Seven Failure Points (FPs) of RAG—from missing content to incorrect specificity—and provides a technical roadmap for mitigation using industry-leading evaluation frameworks like DeepEval, RAGAS, and Arize Phoenix.

Traditional databases excel at keywords but fail at context.

To bridge the gap between structured storage and neural processing, engineers utilize Vector Databases and Vectorization.

This technical deep-dive explains how unstructured data is transformed into high-dimensional coordinates, explores the mathematical foundations of similarity scoring, and provides practical Python implementations for dense, sparse, and hybrid embedding tactics.

Vector search alone is no longer enough for enterprise AI.

While a simple NaiveRAG works for basic FAQs, complex reasoning and multi-document synthesis require specialized pipelines.

This guide dissects the six primary RAG architectures—including GraphRAG and Agentic RAG—and provides a rigorous decision framework to help you choose the right stack for your data’s complexity, reliability requirements, and budget.