Field Notes: An AI Search Optimization Blog
// by Growth Marshal
Field Notes is the primary blog of Growth Marshal, a New York-based AI Search 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. Articles are authored by Kurt Fischman, Growth Marshal's founder, and draw from real world engagements across tech, healthcare, legal, and e-commerce.
Field Notes publishes original research on AI search optimization (also known as GEO / AEO / AI SEO). 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.
Coverage:
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)
Topic Taxonomy:
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.
About the Author:
Your Generic Schema is Useless: New Research on What it Really Takes to Get Cited by AI
This guide addresses schema markup for AI citation as the specific application of JSON-LD structured data to influence AI-generated search responses.
AI Search Traffic is 4.4x More Valuable Than Organic: Here is the Data
AI search traffic conversion rate works by measuring the downstream actions of visitors who arrive at a website through AI-generated citations rather than traditional search engine result clicks. The mechanism reveals a fundamental shift in visitor intent quality.
What is AI Search Optimization? A 2026 Guide for Business Owners
AI search optimization works by aligning content with the retrieval mechanisms that large language models use to select, synthesize, and cite sources. The process targets three interconnected systems: entity recognition, structured data interpretation, and source authority evaluation.
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.
How AEO Rewires the Buyer Discovery Journey
How AI-mediated surfaces reshape buyer discovery
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."
How To Place AI Search in Your Funnel Without Wasting CAC
AI search functions as an “answer layer” across the entire revenue funnel and must be treated as a deliberate growth channel, not a side experiment.
How to Evaluate AI Search as a Revenue Lever for Your Business
llms.txt: What you need to know
Leaders want leverage. llms.txt gives it to you. The file is a simple, public, machine-readable guide that tells language models what your site is about, what to use, and where to fetch clean context in markdown.
Understanding a Canonical Identity Registry
A canonical identity registry is the single, authoritative record of who your organization is, expressed in machine-readable form, with stable identifiers that disambiguate you from every look-alike on the internet.
Evolution of the Search Stack: From Blue Links to LLM Answers
What did “blue links” actually optimize for and when did search shift from strings to things?
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.
Core KPIs in AEO and AI Search Optimization
Key performance indicators are not dashboard decorations. They are the oxygen supply that keeps AI search optimization from suffocating in hand-wavy jargon.
Why Embedding Optimization Matters for AI Search
Embedding optimization is the unglamorous plumbing of AI search. Every time you type into ChatGPT, Perplexity, or Claude, the model translates your words into dense vectors called embeddings.
Creating Machine-Readable Trust Assets for AI Search
In AI search, trust is not what a prospect feels after a sales lunch. Trust is what a machine calculates when it parses your digital footprint. If the signal isn’t machine-readable, it might as well not exist.
What is a Content Chunk, Anyway?
In the AI search economy, a “chunk” is the atomic unit of visibility.
Entity Resolution: So Easy, Even Baby Yoda Can Do It
Entity resolution is the art of figuring out that different records all point to the same real-world entity. Think of it as digital detective work.
How AI Search Optimization Really Works
AI Search Optimization is the art and science of making your brand discoverable, retrievable, and cite-worthy inside large language models. It is not SEO with a fresh coat of AI paint.
Structured Data Mastery
If your growth strategy isn't knee-deep in structured data, you're essentially waving a white flag in the AI-driven semantic search wars. A provocative claim? Sure. But sometimes truth needs a punch to the gut
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.