Large Language Model (LLM)

TL;DR — A large language model (LLM) is the technology behind tools like ChatGPT, Google’s AI Overviews, and Bing Copilot. It works by predicting the next word over and over, based on patterns it learned from a huge amount of text. It doesn’t “know” or “understand” things the way you do — it’s making very good statistical guesses. In search, LLMs read web pages and write the AI answers you see at the top of results.

What an LLM is

Large language models learn statistical patterns over token sequences and generate text by predicting continuations. Evidence for this claim Large autoregressive language models are trained to predict tokens from preceding context and can perform varied language tasks through prompting. Scope: GPT-3 research findings; later models, training methods, and product systems differ. Confidence: high · Verified: 2026-07-14 Brown et al.: Language Models are Few-Shot Learners Product behavior also depends on prompting, retrieval, tools, policies, and model version. Evidence for this claim Applications can give a language model tools for web search, file search, code execution, or external functions. Scope: OpenAI API tool capabilities; tool access is configured separately and should not be conflated with the base model's stored knowledge. Confidence: high · Verified: 2026-07-14 OpenAI: Tools guide

A large language model is a computer program trained on an enormous amount of text — books, articles, websites — until it gets very good at one task: guessing what word (or piece of a word) comes next.

That’s genuinely most of the magic. When you type a question, the model takes your words and predicts the most likely next bit of text, then the next, then the next, building up an answer one piece at a time. “Large” just means it’s huge — trained on billions of examples with billions of internal settings.

The thing to keep in your head: an LLM is predicting, not thinking. It produces text that sounds right because it learned the patterns of how language usually flows. Most of the time that lines up with the truth. Sometimes it doesn’t — and when the model confidently makes something up, that’s called a hallucination.

How LLMs show up in search

When you search and see an AI-written summary at the top — Google calls it an AI Overview — an LLM wrote that. Here’s the rough flow:

1. You type a question.
2. The search engine finds relevant web pages (the normal search step).
3. It hands those pages to an LLM.
4. The LLM reads them and writes a short answer, usually with links to its sources.

The important takeaway for anyone with a website: the LLM is mostly summarizing pages the search engine already found. So if your page can’t be found and indexed in the normal way, it can’t show up in the AI answer either. The technique that feeds fresh web pages to the model is called retrieval-augmented generation (RAG), and it’s how these tools stay current despite being trained on older data.

Which LLMs power which search

• Google uses its own model family called Gemini for AI Overviews and AI Mode.
• Bing / Microsoft Copilot uses OpenAI’s GPT-4 (customized for search).
• Perplexity and ChatGPT Search are other popular AI search tools.
• Claude (Anthropic) and Llama (Meta’s open-source model) are other major LLMs you’ll hear named.

The thing most people get wrong

There is no secret “LLM SEO” that replaces regular SEO. Google’s own people have said the way to appear in AI Overviews is to use normal SEO practices and get indexed. Getting found and indexed is still the prerequisite — the AI layer sits on top of search, it doesn’t go around it.

Want the technical version — the transformer architecture, knowledge cutoffs, context windows, and what my brand research actually found correlates with AI visibility? Switch to the Advanced tab.

TL;DR — An LLM estimates the probability of the next token and generates text autoregressively — that’s the whole engine. It runs on the transformer architecture, which processes a full sequence in parallel via attention rather than token-by-token like RNNs. In search there are two distinct jobs: understanding (BERT-style encoders that help rank) and generation (Gemini, GPT-4 writing AI answers). LLMs are bounded by a knowledge cutoff, a finite context window, and hallucination — which is precisely why RAG and grounding exist. For SEO, indexing remains the prerequisite, and in Ahrefs’ 75,000-brand study by Louise Linehan and Xibeijia Guan text-based signals like branded mentions correlated with AI Overview visibility roughly 3× more strongly than backlinks.

What an LLM actually is

An LLM is not a database of guaranteed facts, and fluent output is not evidence of accuracy. Evidence for this claim Large autoregressive language models are trained to predict tokens from preceding context and can perform varied language tasks through prompting. Scope: GPT-3 research findings; later models, training methods, and product systems differ. Confidence: high · Verified: 2026-07-14 Brown et al.: Language Models are Few-Shot Learners Search products that use LLMs may combine them with external retrieval and ranking systems. Evidence for this claim Applications can give a language model tools for web search, file search, code execution, or external functions. Scope: OpenAI API tool capabilities; tool access is configured separately and should not be conflated with the base model's stored knowledge. Confidence: high · Verified: 2026-07-14 OpenAI: Tools guide

A language model, in Google’s own words, “estimates the probability of a token or sequence of tokens occurring within a longer sequence of tokens.” That’s the foundation. An LLM is a very large version of that: a deep-learning model with billions of parameters trained to predict the next token across web-scale text.

Two things make it “large” and capable:

• Scale. Billions of parameters, trained on enormous corpora. As models grow, capabilities like summarization, reasoning, and code generation start to emerge without being explicitly programmed in.
• The transformer architecture. Introduced in Google’s 2017 paper Attention Is All You Need, transformers process a whole sequence at once and use an attention mechanism so each token can “look at” every other token. Google’s ML Crash Course frames the contrast directly: large language models “can evaluate the whole context at once,” unlike older recurrent neural networks that processed “token by token” and suffered the “vanishing gradient problem.”

The model is trained by next-token prediction on raw text, then typically refined with RLHF (reinforcement learning from human feedback) to make it more helpful and safer. At inference it generates autoregressively — one token at a time, each prediction fed back in as input for the next. A setting called temperature controls how random that sampling is.

It helps to keep the stages separate, because people conflate them constantly. Evidence for this claim Pretraining learns broad statistical representations, post-training changes model behavior toward instructions or preferences, and inference applies the resulting model to supplied context; prompt context does not by itself update model weights. Scope: General pipeline description across instruction-tuned LLMs; the exact post-training method (RLHF, DPO, or other) and how a given product layer handles session memory vary by provider and are not covered here. Confidence: high · Verified: 2026-07-18 Ouyang et al.: Training language models to follow instructions with human feedback Pretraining is where the weights actually get set — the model learns broad statistical patterns from next-token prediction across a huge corpus. Post-training (RLHF and similar preference-tuning steps) adjusts those same weights again, toward instructions and safety behavior. Inference — what happens when you send it a prompt — doesn’t update the weights at all; the model just applies whatever it learned in the two training stages to the text you hand it. That’s also why a long context window isn’t the model “learning” about you: the extra text is input for that one request, not a training update, and it’s gone once the session ends unless a separate product feature saves and re-feeds it as memory.

Keep the mental model honest: this is a probabilistic process. The model produces plausible completions, not verified facts.

Understanding vs. generation: two different jobs

This is the distinction that clears up most LLM-in-search confusion.

• BERT (2019) is an encoder-only, bidirectional model. Google: it considers “the full context of a word by looking at the words that come before and after it.” BERT’s job is understanding — interpreting queries and documents to improve ranking. It doesn’t generate answers. Google said BERT would “help Search better understand one in 10 searches in the U.S. in English,” and Pandu Nayak called it “the biggest leap forward in the past five years.”
• Gemini / GPT-4 are generative models (decoder-style, autoregressive). Their job is generation — synthesizing the AI Overview or Copilot answer text from retrieved passages.

So when an SEO asks “does the LLM rank my page?” the honest answer is: a BERT-style understanding model has long influenced ranking; a generative model like Gemini writes the AI summary over whatever the retrieval step surfaced. Different models, different stages.

Google’s LLM evolution in search

A rough timeline, because the lineage matters:

• 2017 — Attention Is All You Need (Google Research): the transformer paper everything else is built on.
• 2019 — BERT: first transformer LLM in Google ranking; “one in 10 searches.”
• 2021 — MUM: a ~110-billion-parameter, T5-based model Google billed as “1,000 times more powerful than BERT,” multimodal and trained across 75+ languages.
• 2023 — Gemini: “built from the ground up to be multimodal,” pre-trained on multiple modalities from the start; shipped in Ultra / Pro / Nano variants. Google reported it as the first model to “outperform human experts on MMLU” (90.0%). (Benchmark numbers like that one are tied to the exact model version, test set, and evaluation date the lab used at the time — they don’t automatically carry over to later updates of the same model family.)
• 2024 — AI Overviews (graduating from SGE): the generative layer arrives on the results page.
• 2025 — AI Mode + Gemini 3: Elizabeth Reid described Gemini 3 in Search as bringing “state-of-the-art reasoning, deep multimodal understanding and powerful agentic capabilities,” with the system intelligently routing complex questions to Gemini 3 and simpler tasks to faster models.
• 2026 — Gemini 3 becomes the default for AI Overviews. Per Robby Stein, “Gemini 3 is now the default model for AI Overviews.”

Bing Copilot: GPT-4 + Prometheus + the Bing index

Microsoft confirmed in March 2023 that “the new Bing is running on GPT-4, which we’ve customized for search,” and that “as OpenAI makes updates to GPT-4 and beyond, Bing benefits from those improvements.”

The piece that connects a frozen LLM to the live web is Microsoft’s Prometheus model — described as a model combining the fresh Bing index with the reasoning of GPT. The Copilot pipeline reformulates your query into search strings, retrieves from the Bing index, and has GPT synthesize a grounded, cited answer. (Note: that detailed pipeline breakdown comes from third-party technical analysis, not a first- party Microsoft spec — treat the step-by-step as industry-reported.)

How AI Overviews actually generate an answer (the RAG pipeline)

The reason these systems can answer about today’s news despite an old training cutoff is retrieval-augmented generation. Google Cloud’s own definition: RAG “combines the strengths of traditional information retrieval systems with the capabilities of generative large language models.” Conceptually:

1. You submit a query.
2. Complex queries get decomposed — Google’s query fan-out — into sub-queries.
3. Each sub-query retrieves candidate passages from the index.
4. Those passages are injected into the LLM’s context window — this is grounding, anchoring the answer to retrieved sources rather than training data alone.
5. The LLM generates a synthesized answer with citations.
6. Safety and quality checks run, and the answer is returned.

The SEO implication is a chain of gates. Your content has to be (a) crawlable by AI bots, (b) indexed, (c) surfaced by retrieval, (d) selected over competing passages, and (e) represented accurately in the output. Falling out at any stage means you’re not in the answer.

Limitations that matter to SEOs

What this means for your content strategy

A few things I’m comfortable saying, separated from the things nobody outside the engines actually knows:

• Indexing is still the prerequisite. Gary Illyes was blunt: “To get your content to appear in AI Overview, simply use normal SEO practices.” There’s no confirmed special LLM-targeting signal. And on the much-hyped llms.txt file, Illyes said “Google doesn’t support LLMs.txt and isn’t planning to,” with John Mueller comparing it to the old meta keywords tag.
• Brand mentions beat backlinks for AI visibility. In our study of 75,000 brands, branded web mentions were the strongest correlate of AI Overview appearances (≈0.66), versus ≈0.22 for backlinks — text-based signals correlated roughly 3× more strongly than link metrics. As we put it, LLMs “derive their understanding of a brand’s authority from words on the page, from the prevalence of particular words, the co-occurrence of different terms and topics, and the context in which those words are used.”
• It’s winner-takes-all. Brands in the top quartile for web mentions averaged 169 AI Overview mentions versus 14 for the next quartile — and 26% of studied brands had zero. High-authority, high-traffic placements compound your AI visibility.
• Freshness helps. Across a 17-million-citation analysis, AI assistants preferred citing content meaningfully newer than what typically appears in organic results.
• Don’t reflexively block AI crawlers. Blocking likely forfeits AI visibility with no SEO upside. And remember the JS blind spot above — if your content needs JavaScript to appear, most AI crawlers won’t see it.

And the honest caveat: the engines consistently avoid revealing how the ranking side of AI Overviews works. The confirmed story is “get indexed, do normal SEO.” Everything past that is inference — mine included.

AI summary

A condensed take on the Advanced version:

• An LLM predicts the next token and generates text autoregressively — a probabilistic process, not human reasoning. It runs on the transformer architecture (2017’s Attention Is All You Need), which uses attention to process a full sequence in parallel instead of token-by-token like RNNs.
• Two jobs in search: understanding (BERT — encoder-only, bidirectional, helps ranking, doesn’t generate) vs. generation (Gemini, GPT-4 — write the AI answers).
• Training vs. inference: pretraining and post-training (RLHF) are where the model’s weights actually get set; sending it a prompt at inference doesn’t update those weights — a big context window is input for that one request, not memory or a training update.
• Google’s lineage: BERT (2019) → MUM (2021, ~110B params, “1,000× BERT”) → Gemini (2023, multimodal) → AI Overviews (2024) → Gemini 3 as the default for AI Overviews (2026).
• Bing Copilot runs on GPT-4 “customized for search,” bridged to the live Bing index via the Prometheus model.
• AI Overviews use RAG: query fan-out → retrieve passages → ground them in the LLM’s context window → generate a cited answer. Your content must be crawlable, indexed, retrieved, selected, and accurately represented.
• Key limits: knowledge cutoff (≠ release date), finite context window, hallucination (AI tools hit 404s 2.87× more than Google), no JS rendering, and passage chunking (~200-word passages, ~first 30 analyzed).
• For SEO: indexing is the prerequisite (“simply use normal SEO practices”); branded mentions correlated ~3× stronger than backlinks with AI Overview visibility in the 75K-brand study; AI visibility is winner-takes-all; freshness helps; don’t reflexively block AI crawlers.

Official documentation

Primary-source documentation on LLMs in search.

Google

• Google ML Crash Course — Large Language Models — Google’s own definition of a language model and the token-probability explanation.
• Understanding searches better than ever before (BERT) — Pandu Nayak’s 2019 post introducing BERT into ranking.
• Introducing Gemini: our largest and most capable AI model — the December 2023 multimodal architecture announcement.
• Gemini 3 in Search and AI Mode — Elizabeth Reid on reasoning, multimodality, and query fan-out (Nov 2025).
• AI Mode and AI Overviews updates — Robby Stein confirming Gemini 3 as the default for AI Overviews (Jan 2026).
• Retrieval-Augmented Generation (RAG) on Google Cloud — the official RAG framing and how Gemini grounds responses.

Bing / Microsoft

• Confirmed: the new Bing runs on OpenAI’s GPT-4 — Microsoft confirming GPT-4 customized for search (Mar 2023).

Quotes from the source

On-the-record statements from Google and Microsoft. Each link is a deep link that jumps to the quoted passage on the source page.

Google — what a language model is

• “BERT models can therefore consider the full context of a word by looking at the words that come before and after it.” — Pandu Nayak, Google Fellow and VP, Search. Jump to quote

Google — Gemini

• “built from the ground up to be multimodal” — meaning Gemini was “pre-trained from the start on different modalities.” — Google’s Gemini announcement. Jump to quote

Google — Gemini 3 as the default for AI Overviews

• “Gemini 3 is now the default model for AI Overviews, giving you better AI responses.” — Robby Stein, VP of Product, Google Search (Jan 2026). Jump to quote

Google — RAG and grounding

• “RAG is an AI framework that combines the strengths of traditional information retrieval systems with the capabilities of generative large language models.” — Google Cloud, Retrieval-Augmented Generation. Jump to quote

Google — normal SEO for AI Overviews

• “To get your content to appear in AI Overview, simply use normal SEO practices.” — Gary Illyes, Google Search Relations (as reported by Search Engine Land, Jul 2025). Read the coverage

Microsoft — Bing on GPT-4

• “the new Bing is running on GPT-4, which we’ve customized for search.” — Microsoft Bing blog (Mar 2023). Jump to quote

LLMs in search — cheat sheet

Which model does what

Core concepts

• Token — the chunk of text the model predicts (often a word-piece).
• Next-token prediction — the entire generation engine; runs autoregressively.
• Transformer / attention — processes a full sequence in parallel; replaced RNNs.
• Knowledge cutoff — last training date; not the release date.
• Context window — max tokens processable at once; bounds RAG chunk sizes.
• Temperature — controls randomness of the next-token sampling.
• RAG / grounding — feeds fresh retrieved web content into the context window.
• Hallucination — a confident, plausible, wrong completion (a statistical artifact).

SEO fast facts

• Indexing is the prerequisite — “simply use normal SEO practices.”
• Google does not support llms.txt and isn’t planning to.
• Branded web mentions correlated ~3× stronger than backlinks (75K-brand study).
• AI-crawler rendering varies by provider — raw HTML maximizes coverage.

The mental models

1. An LLM predicts, it doesn’t think. Every output is the next-most-likely token given the context so far. “Plausible” is the target, not “true.” Hallucination isn’t a bug bolted on — it’s the same mechanism producing a wrong-but-fluent completion.

2. Two jobs: understanding vs. generation. BERT-style encoders understand queries and documents (ranking). Gemini/GPT-style generators write the answer over retrieved passages. When you ask “how does the LLM treat my page,” first ask which LLM and which stage.

3. The frozen model + the live web. The model’s knowledge is frozen at its cutoff. RAG and grounding bolt on a live retrieval step so the answer reflects today’s pages. Without retrieval, you’re asking a model about a world it never saw.

4. The AI-answer gate chain. To appear in an AI Overview your content must clear five gates in order: crawlable → indexed → retrieved → selected → accurately represented. Diagnose by finding the first gate you’re failing, not by guessing.

5. Mentions over links for AI visibility. The model builds its sense of a brand from words on pages — prevalence, co-occurrence, context. That’s why branded mentions out-correlated backlinks ~3× in the 75K-brand study. Earn talked-about-ness, not just linked-to-ness.

LLM assumptions that lead to bad SEO decisions

Treating fluent output as verified reasoning

An LLM predicts a sequence of tokens, and confidence in the prose is not evidence that a claim is true. Verify consequential claims and inspect the sources supplied by retrieval.

Assuming every model plays the same role in search

Understanding models can help interpret and rank queries while generative models compose answers. Identify the system stage before turning a general model capability into an optimization recommendation.

Replacing crawlability with prompt tactics

Search-grounded systems still need accessible, indexable source material. Clear prompts cannot make an unavailable page enter the retrieval candidate set.

Test yourself: Large language models

Resources worth your time

My related writing

• What We Actually Know About Optimizing for LLM Search — the 75K-brand study, Chrome chunking research, and freshness data.
• An Analysis of AI Overview Brand Visibility Factors (75K Brands Studied) — the full correlation table behind “mentions beat backlinks.”
• LLM Visibility: What It Is and How to Optimize for It — the 17M-citation freshness analysis and the JavaScript rendering blind spot.
• The Complete AI Visibility Guide for SEOs, Marketers, and Site Owners — content formats AI assistants tend to favor.
• What is llms.txt, and Should You Care About It? — why there’s no evidence llms.txt improves AI retrieval.

From others

• Google ML Crash Course — LLM — the clearest official primer on what a language model is.
• Attention Is All You Need (Google Research, 2017) — the original transformer paper that BERT, Gemini, and GPT are all built on.
• Google previews MUM — 1,000× more powerful than BERT (Search Engine Land) — the May 2021 announcement of Google’s T5-based 110B-parameter multimodal model.
• Google says normal SEO works for ranking in AI Overviews (Search Engine Land) — Gary Illyes and John Mueller quotes from the July 2025 Search Central Deep Dive.
• How Microsoft Copilot Search Works: Architecture Deepdive (Rankly) — detailed breakdown of the Prometheus model, the four-stage RAG pipeline, and how Bing bridges GPT-4 to the live index.
• LLM knowledge cutoff dates (allmo.ai) — regularly updated table of training cutoff dates across GPT, Claude, Gemini, and Llama model families.
• r/TechSEO — the community for AI-search and indexing debugging.

Stats worth citing

• Mentions beat backlinks for AI visibility. In the 75,000-brand study, branded web mentions correlated ≈0.66 with AI Overview appearances versus ≈0.22 for backlinks — text-based signals ~3× stronger than link metrics. Source
• Winner-takes-all. Top-quartile brands by web mentions averaged 169 AI Overview mentions vs. 14 for the next quartile; 26% of studied brands had zero. Source
• AI tools hit more dead ends. Across ~16M URLs, AI assistants sent visitors to 404 pages 2.87× more often than Google Search — a hallucination side effect. Source
• AI favors fresher content. Across ~17M citations, AI assistants preferred citing content meaningfully newer than what typically appears in organic results. Source
• AI-crawler rendering varies by provider — JS-dependent content can be missed during retrieval, so raw HTML maximizes coverage. Source

Large Language Model (LLM)

A large language model (LLM) is a deep-learning model trained on massive text corpora to predict the next token and generate human-like text. LLMs use the transformer architecture and power AI search features like Google's AI Overviews (Gemini) and Bing Copilot (GPT-4).

Related: Retrieval-Augmented Generation (RAG), Grounding, Knowledge Cutoff, Embeddings, AI Hallucinations

Large Language Model (LLM)

A large language model (LLM) is a type of deep-learning model trained on huge amounts of text to predict and generate language. Underneath, it does one deceptively simple thing: estimate the probability of the next token given everything that came before, then generate text one token at a time. It isn’t “thinking” or “understanding” the way a person does — it’s running a probabilistic next-token prediction over and over.

What makes a model “large” is scale: billions of parameters trained on web-scale text, built on the transformer architecture Google introduced in the 2017 paper Attention Is All You Need. Transformers process an entire sequence at once and use an attention mechanism to weigh how every token relates to every other token — which is why they outperformed the older, sequential RNN approaches that read text one word at a time.

In search, LLMs do two different jobs. Older models like BERT (an encoder-only, bidirectional model from 2019) help engines understand queries and documents for ranking. Newer generative models — Gemini in Google’s AI Overviews, GPT-4 in Bing Copilot — generate the synthesized answers you see, usually by combining their training with fresh retrieved web content via RAG and grounding. Knowing which model is doing understanding versus generation is the single most useful distinction for SEOs trying to reason about AI search.

Related: Retrieval-Augmented Generation (RAG), Grounding, Knowledge Cutoff, Embeddings, AI Hallucinations

Changelog