How AI Search Works — RAG, Training, and What It Means for Your Brand

The model's base knowledge

How it works

During pre-training, LLMs ingest vast quantities of web content — articles, documentation, forum posts, news. Brands, products, and concepts that appear frequently, consistently, and accurately in that training corpus become part of the model's base knowledge. This knowledge is static until the model is retrained.

What to do

To influence training data signals: build consistent, authoritative content published across many sources over time. Wikipedia mentions, press coverage, and well-linked documentation all contribute.

Real-time web retrieval

How it works

Many modern AI systems — including Perplexity, ChatGPT with web browsing, and Google AI Overviews — use RAG: they fetch current web pages at query time, extract relevant passages, and incorporate them into the generated answer. This is more dynamic than training data and can be influenced by current on-page content.

What to do

To perform well in RAG: ensure key pages are crawlable by AI agents (check robots.txt), load fast, and contain direct answers to target questions. Structured data and clear semantic HTML improve extraction quality.

Human preference training

How it works

Models are refined using Reinforcement Learning from Human Feedback (RLHF) — human raters evaluate responses, and the model learns to prefer answer styles that humans rate as accurate, helpful, and trustworthy. This shapes which types of content and sources the model gravitates toward.

What to do

High-quality, trustworthy, well-cited content performs better because it matches the response style RLHF training reinforces. Thin, marketing-heavy content without supporting evidence is deprioritised.