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There Are Two Types of LLMs: Generative vs Retrieval-Based Explained

In this short video, we break down the two primary types of large language models (LLMs) shaping how people find answers today:

Generative LLMs like ChatGPT and Claude

Retrieval-based LLMs like Perplexity and Google SGE

Understanding the differences between these models is critical for B2B marketers, SEO professionals, content creators, and founders who want their content to be cited by AI tools, not just listed in old-school search engine results.

This video explains how each model works, how they treat new content, and why it matters if you're trying to appear in AI-generated answers.

What You’ll Learn in This Video:
What a generative LLM is and how it uses pre-trained data to generate answers

What a retrieval-based LLM is and how it fetches real-time web content

How long it takes for your content to appear in each type of model

Why your content may be invisible to AI unless it's properly syndicated and structured

What LLM SEO means and how it's different from traditional SEO

Examples Covered in the Video:
ChatGPT (OpenAI)
A generative model trained on large datasets. It does not access the live internet by default. Content may take months to be reflected unless OpenAI includes it in a model update.

Claude (Anthropic)
Another generative model focused on safe, accurate responses. Claude operates similarly to ChatGPT, using static training data that’s periodically updated.

Perplexity.ai
A retrieval-based LLM that fetches and cites live web content in real time. Perplexity is ideal for content that is syndicated across trusted, high-authority sites and portals.

Google SGE (Search Generative Experience)
Google’s AI-powered search feature that summarizes content from the web and presents AI-generated answers directly in search results. It updates frequently and rewards structured, authoritative content.

You.com
A hybrid AI search assistant that pulls in results from real-time web data and blends it with LLM-powered summaries.

Why This Matters:
B2B buyers and researchers are increasingly turning to AI assistants instead of traditional search engines. If your content is not discoverable by retrieval-based models, you won’t show up in real-time answers. If it’s not widespread or authoritative enough, generative models won’t include you in their next training cycle.

The difference between these two LLM types determines:

How fast does your content become discoverable

Where your content appears (chat tools, AI summaries, or not at all)

Whether your brand is part of the buying conversation

How to Prepare for LLM Visibility:
Syndicate your content to trusted third-party platforms

Structure your content using H1/H2 headers, FAQs, and summaries

Repeat key phrases using canonical brand language

Optimize for retrieval-based tools to gain fast visibility

Build LLM SEO workflows for inclusion in future model updates

Whether you're publishing case studies, whitepapers, blog posts, or buyer guides, your content must be formatted and distributed to work for both types of models.

If you’re still optimizing only for Google, you’re missing how today’s buyers actually discover solutions.

Watch this short to learn how to make your content LLM-ready and stay ahead of the shift from search to answers.

Subscribe for more insights on LLM SEO, AI discoverability, and next-gen B2B marketing.

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