RAG

TL;DR: Just ask the LLM “from where to where,” and let the server retrieve the text directly. Measured on 3 pages: 90% fewer output tokens, 87% lower latency, 61% cost savings. Background: From Docling to PyMuPDF + VLM While building an AI system for architecture regulation review, we needed to split architecture notice/guideline PDFs into semantically meaningful chunks. These chunks serve as retrieval units in a RAG pipeline. We initially used IBM’s Docling, which uses OCR models to analyze document structure before chunking. However, we ran into two problems: ...

Author: Kim Bo-geun What happens when a building code review AI confuses “4 floors or less” with “4 floors or more”? The height limit gets inverted, and an illegal building gets judged as legal. This article is about the journey to catch that single-character difference. The Problem: Tables Are Retrieved but Unreliable The building code review system analyzes building-related PDFs — district unit plans, design guidelines — to extract standards like building coverage ratio (BCR), floor area ratio (FAR), and height limits. The PDF preprocessing pipeline uses Docling to parse documents, chunk text, and generate embeddings for hybrid search (keyword + semantic). ...

Kyung Nong FarmingNote Enhancement Project — Sharing the design and implementation of an AI system that automatically searches product information from a single pesticide product photo. Project Background Kyung Nong had previously built a generative AI-based agricultural chatbot with AWS and Megazone Cloud. Using a RAG architecture with Amazon Bedrock Claude Sonnet 3.5 and OpenSearch, the service automatically responded to crop protection product queries in natural language. While operating this chatbot, we received meaningful field feedback and a new proposal from Kyung Nong. Given that many elderly farmers work in the field, typing long and unfamiliar pesticide product names on smartphones was very cumbersome. ...

1. Evaluation Overview Objective: Select embedding models optimized for Korean statutes and ordinance search (RAG) systems Evaluation Dataset KCL-MCQA (Korean Canonical Legal Benchmark) 282 questions, 867 case law documents (expert-tagged Ground Truth) Rationale for Data Selection Currently, no public benchmark dataset exists for Korean statutes/ordinances KCL-MCQA is the only verified Korean search evaluation dataset in the legal domain Case law and statutes/ordinances share identical legal terminology and writing style, enabling similar embedding performance expectations Re-evaluation recommended when statute/ordinance-specific evaluation datasets are built Evaluation Environment ...