Before we talk about how AI makes decisions, we need to clarify what is making the decisions.

You have probably heard terms like LLM and RAG. These are the engines behind modern AI answers. This chapter explains them in plain language, then shows how content gets crawled, selected, and generated into answers you see in AI platforms like ChatGPT, Claude, and Perplexity.

So grab some snacks, and let’s begin.

LLM Core

LLM stands for Large Language Model. Think of an LLM as a very advanced text engine trained on huge amounts of data so it can understand prompts and generate human-like responses. This is the core of most AI systems.

Limits:

• Knowledge cutoff – Models are trained up to a point in time. As of today, the cutoff for ChatGPT is June 2024, and for Claude and Perplexity, it’s January 2025. They do not inherently “know” real-time events beyond those dates.
• Bounded memory – They are bounded by memories from their training sessions. They cannot give you real-time information. 
• Hallucinations – When asked about things outside their training or current context, they may produce confident but false answers.
  

Example:
Ask “What is the capital of India?” and you get New Delhi instantly, because that fact is part of the model’s learned knowledge. Ask “What is the weather in New Delhi today?” and the Core LLM cannot answer as It does not have real-time data by itself. That’s where RAG comes into play.

RAG: The Live Context

RAG stands for Retrieval-Augmented Generation. It pairs the LLM Core with a retrieval system that pulls information from external sources when the answer for the particular query is not available in its knowledge database. 

The LLM then uses those fresh documents as context to generate a grounded, up-to-date, human-like text/response.

Simple flow:

1. Retrieve relevant documents from the open web or a private knowledge base.
2. Augment the LLM with those documents as context.
3. Generate an answer that cites or reflects the retrieved sources.
   

Ask “What is the weather today in New Delhi?” and a RAG-enabled system can fetch current weather pages, then produce a factually correct answer.

Note: It is important to point out that LLM and RAG are NOT two different systems. Today, all AI systems or LLMs in combination with RAG are how they make decisions and give live, up-to-date, factually correct information. 

LLM Core and LLM + RAG (Summary)

Aspect	LLM Core	LLM + RAG (Retrieval-Augmented Generation)
Knowledge source	Fixed, learned during training	Pulls from external documents or APIs at query time
Updating knowledge	Requires new training or fine-tuning	Reads fresh sources dynamically, no retrain needed
Context handling	Limited to model context window	Extends context by retrieving relevant passages
Grounding and accuracy	Prone to hallucinations on niche or recent topics	More factual when good sources are retrieved and cited
Dependence on external data	None after training	Yes, relies on access to indexes, databases, or the open web
Strengths	Fluent writing, summarization, general conversation	Precise answers in specialized or time-sensitive domains

Why Content Teams Should Care About What Makes The Decisions?

From a content perspective, your near-term leverage is retrieval: make your public pages easy for AI answer engines to discover, select, and quote. 

It is much easier to influence AI’s live search process than to influence the core LLM weights. Over time, as your authority grows, future LLM training runs may also absorb your content into the model’s long-term memory. But the practical starting point is influencing AI’s live search.

To do that, you must understand how your content is discovered, selected, and used in generation.

How AI Decides and Ranks Your Content?

1) Query Processing

A user asks: “We’re a 150-person B2B team choosing the best contact database tool in 2025. Must have accurate B2B emails, LinkedIn enrichment, CRM sync, and under $30k.”

The AI first extracts intent and details: team size, must-haves (email accuracy, enrichment, CRM sync), limits (budget, year), and context (B2B). It then turns that into structured data, almost like a checklist, so it knows exactly what to look for: tools that fit 2025 pricing, data quality needs, integrations, and budget.

Next comes the process called query fan-out. Instead of running a single broad search, the AI splits your main question into many smaller, targeted searches such as “best contact database tool” “contact database tool” followed by deeper search terms like “ZoomInfo 2025 pricing 150 seats,” “Apollo data accuracy benchmarks,” “Lusha GDPR and consent,” “Clearbit CRM integrations,” “Seamless.AI credits per contact,” “Reddit B2B data coverage US vs EU,” and “G2 mid-market contact database comparisons.” 

By running all these smaller searches in parallel, the AI collects detailed information from multiple reliable sources that match the topic, timeframe, and user needs. Past behaviour and preferences can steer which sources get prioritised (indicating that responses might differ for multiple users).

2) Crawling: How your content becomes discoverable

Here’s a side-by-side breakdown of how each AI model crawls and discovers content:

ChatGPT vs Claude vs Gemini vs Perplexity

Dimension	ChatGPT / OpenAI	Claude	Gemini / Google	Perplexity
Primary external index used for discovery/ranking	Relies primarily on Bing’s index.	Primarily Brave Search powers Claude. Followed by Google.	Google Search.	Relies heavily on Google’s index.
Own crawler role (not a public web index you can “SEO” like Google/Bing)	OAI-SearchBot + ChatGPT-User fetch for freshness/caching; discovery still largely via Bing.	Claude-SearchBot + Claude-User fetch/cite from Brave and Google results.	Uses Googlebot stack; Gemini itself doesn’t run a separate public crawler.	PerplexityBot + Perplexity-User fetch/cite; docs publish IPs.
JS rendering expectation	Assume no JS rendering → expose key copy via SSR/SSG.	Assume no JS rendering → favour static HTML.	Full JS rendering via Googlebot; SPAs OK if SEO-friendly.	Generally no JS rendering → ship static HTML.
Robots controls (training vs visibility)	Block GPTBot (training) while allowing OAI-SearchBot / ChatGPT-User.	Block ClaudeBot; allow Claude-SearchBot / Claude-User.	Use Google-Extended to opt out of Gemini training without affecting Search.	Allow PerplexityBot (publishes IP JSON) for inclusion.
On-demand user fetch	Yes (ChatGPT-User)	Yes (Claude-User)	Handled by Google (no separate “Gemini-User” UA).	Yes (Perplexity-User).
IP/User-Agent (UA) transparency	Distinct UAs; IPs not broadly published.	UAs documented; IP ranges not public.	Mature Googlebot docs.	UA + live IP JSON published.
How sources are ranked	Relies on Bing’s index; ChatGPT then picks a few of those pages to cite.	Relies on Brave’s index and sometimes Google; Claude fetches from that set and cites.	Google Search ranks; AI Overviews summarize from top Google results.	Mostly relies on Google’s index; Runs many searches, reads a lot, then builds answers from the most credible/official sources.
Heavily cited content types	Wikipedia dominates; also review/media (G2, TechRadar, NerdWallet, Forbes, Reuters) and some Reddit (declining lately).	Brave-indexed review sites & retailer/product guides; structured specs/comparisons; less default UGC (Reddit/YouTube surfaces mainly when directly relevant).	Reddit, YouTube, Quora, LinkedIn, Gartner frequently appear; strong mix of social + pro/analyst sources.	Reddit is disproportionately cited; plus YouTube, LinkedIn, G2; excels with official docs/regulations in “Deep Research” tests.
Best technical bet for marketers	Win Bing-style queries + be crawlable to OpenAI bots; don’t assume OpenAI fully re-ranks the web – provider ranking seeps through.	Optimize for Brave search signals (clean, lightweight pages; strong entity clarity), plus allow Claude’s crawlers.	Classic Google SEO (E-E-A-T, CWV, structured data) is the lever; JS-rendered content is fine because Google renders.	Publish primary “source-of-truth” pages: official docs, specs, policies, step-by-steps – lightweight, structured, easy to cite.

3) Retrieval and Ranking

Given a query, the system searches its indexes and scores candidate documents. Traditional SEO leans on keywords and backlinks. AI platforms emphasise semantic relevance and source authority. It tries to fetch trustworthy, context-relevant content, not just pages with exact-match phrases.

How AI platforms select sources?

What tends to matter:

• Intent match. The closer your page aligns with the query’s topic and task, the better.
• Credibility. Reputable domains, expert authorship, and consistent topical depth perform well.
• Comprehensiveness. Long-form pages that cover a question thoroughly tend to beat thin posts.
• Structure. Clear headings, lists, tables, and definitions make extraction easier. Listicles and ranked summaries are especially digestible and “quote-ready.”
• Plain HTML. Bots struggle with heavy client-side rendering and hidden elements. Expose important text in static HTML.
• Schema. Structured data helps machines interpret meaning and entities cleanly.
• Evidence. Citations, data, case studies, and reviews increase trust.
• Links and mentions. Being cited across credible blogs, forums, and directories boosts discoverability and confidence.
• Technical health. Clean code, sitemaps, no crawl barriers, and fast load times create a solid baseline.
• Freshness. New or recently updated content often gets preference for time-sensitive queries.

3 Key Tactics to get Retrieved and Mentioned by AI platforms:

1. Clear, structured, high-quality content that answers user queries and a consistent topical authority.
2. Offsite citations
3. Technical SEO done right

We discussed each tactic in detail in later chapters. 

4) Generation: Turning sources into human-like responses

Finally, the LLM takes the selected documents and writes the response. Good AI setups:

• Keep the model within the bounds of the retrieved facts.
• Prefer direct quotes or paraphrases with citations.
• Preserve user intent and style instructions while staying accurate.

Quick Checklist for AISEO Teams:

Is the key content visible in static HTML without running JavaScript?
Do we have intent-focused titles, H1-H3s, and answer blocks that map to common questions?
Are we using schema for the page type and entities?
Do we provide evidence: data, case studies, quotes, or references?
Are we earning third-party mentions and list inclusions in our niche?
Is the page technically clean: fast, indexable, in sitemaps, with no crawl barriers?
Is the content fresh and updated for time-bound topics?

End of Chapter 3

In this chapter, we discussed how AI decides and ranks your content. In order to influence AI’s decision-making process (AISEO) and get retrieved and mentioned by AI, there are 3 Key tactics: Clear and comprehensive content used to develop consistent topical authority, credible citations/backlinks, and solid technical SEO. We break each of these down in detail in the chapters ahead.

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