AI

Working notes on the AI side of the practice.

Featured

• Building a Large Language Model From Scratch: Architecture, Components, and Implementation Guide — Building a Large Language Model From Scratch: Architecture, Components, and Implementation Guide
• The Open-Source AI Model Landscape: Architecture, Benchmarks, and Enterprise Deployment — The Open-Source AI Model Landscape: Architecture, Benchmarks, and Enterprise Deployment

Sections

• Agents & Tools — Agentic patterns, tool design, and orchestration.
• Document Intelligence — Document AI — chunking, layout, extraction, and the Optey lineage.
• LLMs & Foundation Models — Notes on LLMs, foundation models, and their integration.
• Prompt Engineering — System prompts, templates, and evaluation harnesses.
• RAG & Retrieval — Retrieval-augmented generation patterns and lessons.

Articles

• AI Agents Guide: A Strategic White-paper — AI Agents Guide: A Strategic White-paper
• AI Model Architecture Landscape: A Decision Framework for Commercial vs. Open Source Deployment — AI Model Architecture Landscape: A Decision Framework for Commercial vs. Open Source Deployment

What is published here: hands-on working notes on LLMs and foundation models, retrieval-augmented generation, document intelligence, agents and tooling, and prompt engineering — much of it learned building this site’s own RAG pipeline and tools. What is not: proprietary model internals or any client-confidential data; the focus is reusable technique, not a specific employer’s systems.