AI signal, minus the noise.

This Medium tutorial is Part 3 of a series on constructing a production-grade LLM memory system. The accessible content only shows a teaser linking to the previous article and a prompt to continue reading on Medium. The title suggests the tutorial covers the integration of FastAPI, short-term memory (STM), long-term memory (LTM), and retrieval-augmented generation (RAG), but no concrete technical details are available from the raw feed content, which is limited to a brief promotional snippet.

This Towards Data Science tutorial discusses using vision language models to parse charts, diagrams, and other visual elements from PDF documents. It shows how these models extend beyond text-only parsing, allowing retrieval-augmented generation (RAG) systems to incorporate image-based information. The post focuses on practical integration of visual context into enterprise document intelligence workflows.

In this blog post, the author benchmarks retrieval-augmented generation (RAG) pipelines against a deterministic full-scan engine across 100,000 rows for aggregation tasks. The results show that larger context windows do not improve accuracy—they actually make errors harder to detect. The author finds that computation-heavy queries must be routed away from RAG entirely, and builds a system that directs such queries to a deterministic full-scan engine to preserve accuracy.

The tutorial shows how to parse PDFs locally using the Docling tool, preserving table cells, OCR text, captions, and headings. The output matches cloud-grade document structure without any cloud upload, API keys, or per-page billing. This approach enables privacy-preserving document intelligence for RAG pipelines by converting PDFs into richly structured data ready for ingestion.

This tutorial from the Enterprise Document Intelligence series shows how Azure Document Intelligence’s layout model extracts relational tables from PDFs where PyMuPDF falls short. The Azure approach preserves native table cells and works on scanned pages via integrated OCR. It also retrieves captions and headings without relying on regular expressions. The method is presented as a superior parsing step for Retrieval Augmented Generation (RAG) pipelines.

An independent AI consultant developed a three-tier system for a 400-employee precision parts manufacturer near Osaka to address the impending retirement of two veteran inspectors with 99.7% accuracy. First, 60 hours of inspectors' verbal explanations were recorded and transcribed using LLMs to build a retrievable knowledge base of tacit diagnostic logic. A RAG system enables new inspectors to query similar cases, while a vision AI model classifies defects with root cause suggestions. After five months, A/B testing showed AI-assisted new inspectors reached 99.2% accuracy, up from 96%, and the knowledge base was adopted by production engineers. The project reframed the initial request from a simple AI visual inspection to a knowledge preservation system.