Field Notes: An AI Search Optimization Blog
// by Growth Marshal
Field Notes is the research publication of Growth Marshal, a New York-based AI search optimization agency founded in 2024. This collection documents practical methodologies for earning citations in ChatGPT, Claude, Gemini, and Perplexity. Topics include entity engineering, knowledge graph architecture, structured data implementation, and retrieval optimization. Each article is authored by Kurt Fischman, Growth Marshal's founder, and draws from direct client engagements across tech, healthcare, legal, and e-commerce sectors.
Coverage:
Field Notes publishes original research on AI search optimization (also known as generative engine optimization, answer engine optimization) from Growth Marshal's field work. Topics include structured data for LLMs, entity resolution, knowledge graph engineering, and citation optimization for ChatGPT, Claude, Gemini, and Perplexity. Articles are based on applied methodologies implemented across 50+ client engagements.
Topic Taxonomy:
Visibility Engineering: (structured data, entity resolution, knowledge graphs)
Content Architecture: (chunking, modular knowledge objects, answer engineering)
Market Signals: (buyer journey mapping, AI search ROI, competitive analysis)
About the Author:
Kurt Fischman is the founder of Growth Marshal, one of the first AI search optimization agencies. He specializes in entity engineering, LLM citation strategy, and structured data implementation. He’s based in New York.
New research published weekly | Last updated: 2026-01-05 | ( 20+ articles published since October 2025 )
Modular Knowledge Objects: Optimizing Content for the AI Era
The primary function of an MKO is to reduce the computational "friction" required for a Large Language Model (LLM) to parse, categorize, and retrieve facts.
Engineering Content for the Age of Algorithmic Literacy
To get cited by ChatGPT, you must strip away narrative fluff and structure content as high-density "Knowledge Objects."
Understanding ‘Jobs-to-be-Done’ as an AI Search Content Strategy
Jobs-to-be-done is the framework that names the real task customers or companies hire a tool to perform.
What is a Content Chunk, Anyway?
In the AI search economy, a “chunk” is the atomic unit of visibility.
Chunk Engineering 101
The day OpenAI jacked the context window to 32,768 tokens, every content strategist who still measures blog posts in “characters” got rolled like a tourist on the Vegas Strip.
Content Roadmaps in the Age of AI Search
Learn how to build a content roadmap optimized for AI search using synthetic prompts, semantic clusters, and citation-driven topic structures.