How search really works now — and how to be found in it
A complete, self-contained guide to generative engine optimisation: how AI search retrieves, chunks, evaluates and cites information, and how to become a source an AI answer names.
Free download. No payment, no sign-up. Marketers, SEO specialists, content leads and founders. No coding background needed.
AI search stopped handing people a list of links and started writing them an answer. The AI Search Handbook explains the machinery underneath that answer — crawling, indexing, retrieval, chunking and citation — and turns it into practical craft you can apply on Monday.
It is deliberately unexcited. Where the evidence is strong, the book says so. Where a widely repeated tactic has no support, or has been contradicted by data, it says that too, and shows you how to judge the next claim yourself.
Luke is an AI marketing and search specialist with more than ten years in B2B marketing across biotech, life sciences and SaaS. His work sits where technical SEO meets generative engine optimisation: making brands crawlable, retrievable and cited by AI answer engines.
He holds an MSc in Molecular Biology, works hands-on in Python, SQL and both cloud and local open-weight language models, and writes these books so that marketers can do the same.
Sixteen chapters from NumPy and data preparation through classical machine learning, neural networks and PyTorch to NLP, embeddings, transformers, RAG and MLOps — every project with runnable code and its exact output.
A hands-on course in fourteen chapters. Each project is a small Python audit tool that crawls, parses and checks a site the way a search engine does — ending with GEO, AEO and an end-to-end technical audit.
An illustrated learning path for marketers who want to understand the instructions and build something useful: 52 chapters across ten parts, with a companion pack of runnable exercises and solutions.
