ABSTRACT
How to engineer web content whose passages are selected, trusted, and reused during AI answer synthesis. A strategic and operational analysis for advanced practitioners. This paper provides a strategic, operational, and tactical analysis of MKA for practitioners who already understand the landscape. It is not an introduction to AI search. It is a deep reading of the doctrine's architecture, its operational logic, and the places where it breaks new ground relative to prevailing practice.
Section 01
The core innovation of MKA is a reframe. It does not ask how to write pages that AI systems will cite. It asks a harder, more consequential question: how do you engineer passages whose upstream determinants make citation a likely downstream artifact?
That distinction matters because it relocates the optimization target. Most AI search optimization methodologies focus on surface formatting: FAQ blocks, numbered lists, schema markup, keyword placement. MKA treats those as second-order effects. The first-order concern is whether a passage contains enough informational density, local coherence, and evidentiary support to survive the retrieval pipeline on merit.
Core Thesis
Citation is not the goal. It is the receipt. Selection-worthiness, trust, and synthesis fitness are the upstream forces that produce it. Engineer for those, and citation follows.
This paper provides a strategic, operational, and tactical analysis of MKA for practitioners who already understand the landscape. It is not an introduction to AI search. It is a deep reading of the doctrine's architecture, its operational logic, and the places where it breaks new ground relative to prevailing practice.
Download to keep reading ↗️
@techreport{growthmarshalgm2026005,
author = {Fischman, Kurt},
title = {Modular Knowledge Architecture: A Passage-Selection Doctrine for AI Retrieval},
institution = {Growth Marshal},
year = {2026},
number = {GM-2026-005},
url = {https://www.growthmarshal.io/research/mka}
}
Get recommended by answer engines, automate more work, and build the foundation you need to compete in an AI-first market.