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 )
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.
Mentions > Links: A New “Ranking” System for AI Search
Discover how LLMs “rank” brands using positive context, not backlinks. Sentiment-weighted mentions are a major driver of AI visibility.
Embedding Visual and Audio Assets for LLM Retrieval: A Field Manual to Multimodal Immortality
Learn how to embed and tag visual and audio assets for LLM retrieval. Discover the tools, models, and metadata standards that drive AI discoverability and multimodal search.
Answer Shapes 101
Answer Shapes—the atomic units of information machines can parse, lift, and redeploy without mangling meaning.
The 2025 Perplexity Playbook: Sonar Ranking Factors
Reverse-engineered insights on Perplexity’s Sonar model. Discover how freshness, PDFs, and FAQ markup drive LLM citations and zero-click visibility.
Why Brand, Distribution, and Trust Are the Last Moats
LLMs have collapsed software moats. Learn why brand authority, distribution, and trust are now the only paths to business survival.