Welcome to    by Kuriko IWAI.

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.

Small models trade off intelligence for efficiency.

This technical deep-dive demonstrates how to bridge that gap.

By utilizing a three-phase training pipeline—Supervised Fine-Tuning (SFT), Response Knowledge Distillation (RKD), and Direct Preference Optimization (DPO)—we embed complex human traits into a small model, then deploy it across a high-throughput AWS SageMaker environment and privacy-first edge devices.