If you’ve been too busy to dive into all the discussion around the new search paradigm, here are quick summaries and pointers from which you can work 👇
📌 AI builds answers by combining passages from multiple sources.
Write in modular blocks that are independently useful.
📌One query often triggers multiple related prompts.
Use tools like People Also Ask and AlsoAsked to surface longer-form questions and match your content to intent.
📌Search systems split content into passages and store them individually.
Structure your writing so each section delivers on a specific idea.
📌Text is converted into vectors that capture its meaning.
You can test semantic similarity using the OpenAI Embeddings API to see how your content aligns with target queries.
📌Similarity is measured by how close those vectors are to the query.
Well-phrased examples and direct answers increase the chances of retrieval.
📌Search results shift depending on who’s searching.
Writing for specific personas makes your content more likely to be surfaced.
📌Traditional SEO tools focus on different scoring systems.
Explore tools like Vectara, Haystack, or LlamaIndex to test retrieval using AI-native methods.
📌AI systems chunk text by token count.
Use clean formatting, consistent flow, and focused paragraphs to support chunk-level retrieval.
These are some of the working ideas behind modern search systems. Some are already influencing visibility. Others are early-stage observations waiting to be tested.
The only way to know what works is to test it.
What did I miss?