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:
A Simple Guide to Understanding Embeddings
Embeddings are the hidden geometry beneath modern artificial intelligence. They are not visible to the end user, but without them, large language models (LLMs) would not be able to represent or compare meaning.
Entity-Centric Architecture 101
Entity-Centric Architecture defines the way modern knowledge systems survive the onslaught of digital chaos. And without it, you’re left with the informational equivalent of a Baghdad, circa 2006: tangled wires, collapsing roofs, and a thousand alleyways leading nowhere.
How Wikidata Enables AI Search Optimization
For large language models and retrieval systems, Wikidata is a primary evidentiary layer.
Intro to AI Search Optimization
AI Search Optimization exists because the world’s information plumbing got rerouted in 2023
How ChatGPT Inclusion Works for Companies
Executives love management-speak the way moths love lightbulbs. “Inclusion in ChatGPT” has become one of those phrases.
AI-Native Lead Capture: From Architecture to Execution
The conversation, the qualification, and the capture can all happen inside that black-box dialogue.
Sales Attribution from LLMs: Counting the Invisible
Why does LLM attribution feel like old-school advertising?
Monetizing AI Visibility: The Basic Principles
The next frontier of monetization isn’t paid ads, backlinks, or even SEO. It’s getting your brand named, linked, or cited by generative models—and converting that visibility into cash.
AI Search Optimization: A Technical Definition
AI Search Optimization is the discipline of making information discoverable, retrievable, and cite-worthy by LLMs.
Engineering Answer Coverage: Mapping Prompt Surface Patterns in AI Search
On one side is the way a question is phrased: the prompt surface pattern. On the other is the way the model knows how to reply: the answer shape.
The Importance of Entity Salience in AI Search: From Mentions to Meaning
Understand why entity salience separates winners from the content herd.
What is an AI Search Optimization Agency?
It sure isn’t a rebranded SEO shop with ChatGPT stickers slapped on.
Trust Signals in AI-Driven Rankings: Why Authority is the New Currency of Visibility
Learn how Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems evaluate trust. Discover the new rules for entity consistency, knowledge graphs, schema markup, and AI-native content authority.
Authority Building for LLM Credibility
To stay relevant, companies need to establish authority that LLMs recognize, just like they do with traditional search engines. The strategies overlap, but the nuances matter.
The Ultimate Guide to Building AI-Era Authority
Learn how structured data, entity linking, citations, and author trust fuel AI search visibility. Build a Trust Stack LLMs can't ignore.
Measuring AI Visibility: A Step by Step Guide to Citation Analytics
Learn how to track AI citations, embedding alignment, and surface visibility across ChatGPT, Perplexity, and Claude. This guide to AI visibility benchmarking helps you build a scoreboard for the post-Google search era.
Competitive Intelligence in AI Search
Your competitors are winning AI citations in ChatGPT and Perplexity. This guide shows how to reverse-engineer their strategy and claim the citation space that matters.
Wikipedia or Die: How to Claim Your Q‑Node and Own LLM Entity Disambiguation
Missing from the Wiki? You’re invisible to LLMs. Learn how to seed Wikipedia, claim your Q‑node, and control how AI models define your company.
Use Public Repos to Pull Your Company into ChatGPT Answers
Learn how to structure your GitHub repo, README, and code examples to get cited in GPT-4o and other LLMs. Seed once, surface forever.
How to Use Endpoints to Drive LLM Citations
Transform your company's facts into AI-ready data. Learn how JSON‑LD endpoints drive crawlability, LLM citations, and long-tail search dominance.