Skip to main content

Overview

A curated library of deep-dive guides, mental models, and practical patterns for building reliable, secure, and scalable systems.

This collection is organized as focused tutorials and reference notes — each entry is designed to be read independently but together they form a practical playbook for enterprise architects and senior engineers.

What you'll find here​

  • Clear mental models that explain how systems behave at scale (LLMs, RAG, agentic systems).
  • Practical patterns for cloud and platform modernization, microservices, event-driven design, and API-led architectures.
  • Operational guidance: governance, risk, compliance, and platform operating models for regulated environments.
  • Integrations and engineering patterns for embedding AI/GenAI safely into enterprise systems.
  • Developer enablement, observability, and platform engineering practices that improve delivery and reliability.

Key topics (short summaries)​

LLM (Large Language Model)​

A mental model of how Large Language Models work, what they can and cannot do, and how to design systems around their constraints.

RAG (Retrieval-Augmented Generation)​

How to ground LLMs with external data, design retrieval pipelines, and balance cost/latency/accuracy when building production-ready RAG systems.

Agentic AI​

Patterns for composing LLMs with tools, orchestrators, and decision logic to safely automate workflows and integrate with enterprise systems.

MCP (Model Connector/Platform patterns)​

Standards and interfaces for securely connecting models to tools, data, and services while preserving governance and auditability.

Cloud & Platform Modernization​

Guidance for migrating, modernizing, and operating platforms across AWS, Azure, and hybrid environments with an emphasis on security and scalability.

System Design & Distributed Systems​

Architectural patterns for resilient services: partitioning, consistency, backpressure, observability, and failure recovery at scale.

Governance, Risk & Compliance​

Frameworks for policy, data protection, monitoring, and compliance when deploying AI-enabled capabilities in regulated industries.