What is retrieval-augmented generation?

Retrieval-augmented generation (RAG) is the process by which a large language model, instead of relying solely on its training data, fetches relevant external documents at query time and uses them to generate a more accurate, up-to-date answer. The model retrieves, then it reads, then it responds. The cited sources shape the answer the user sees.

See how Simaia gets your brand into those cited sources →

Stat strip:

• RAG powers the cited answers in ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview.

• Simaia's Healthcare SaaS client grew from 0% to 45% AI search visibility in 2.5 months.

• A global textile manufacturer saw AI bot visits grow 3.5x year-over-year after Simaia began publishing LLM-optimised content.

How does retrieval-augmented generation actually work?

When a user asks a question, the LLM queries a retrieval system (a vector database, a web index, or a curated knowledge store) to pull in documents relevant to that query. Those documents become the model's context window. The model synthesises an answer from that retrieved context, and often surfaces the source as a citation.

The practical implication: if your brand's content is not in the sources the model trusts and retrieves, your brand does not appear in the answer, regardless of your Google ranking.

The three-step RAG loop:

• Retrieve. The model identifies and pulls authoritative, relevant documents from trusted sources.

• Augment. Those documents are injected into the prompt as grounding context.

• Generate. The model writes an answer from that context, attributing sources the user can see.

Why does RAG matter for B2B buyers specifically?

B2B buyers increasingly start with a question, not a search query. They ask ChatGPT or Perplexity which supplier to evaluate, which software category to shortlist, or which vendor handles their specific problem. The model retrieves external content to answer that question. Whoever owns the retrieved sources owns the recommendation.

For B2B companies in categories like manufacturing, SaaS, HR outsourcing, or professional services, a competitor appearing in an AI answer is a referral at scale, unprompted and without ad spend.

What content gets retrieved by LLMs, and how do you get into it?

Each major LLM has source preferences. ChatGPT retrieves from LinkedIn frequently. Google AI Overview retrieves from Reddit. Perplexity and Claude weight editorial publications, industry sites, and long-form structured content. Content written for traditional SEO, optimised for keyword density and backlinks, does not automatically translate into LLM retrieval.

Content that gets cited is structured for extraction: clear claims, specific facts, direct answers to question-format queries, and placement on the platforms each model trusts.

Proof:

• Simaia published 90 LLM-optimised blog posts in a client's first month.

• A press release was picked up by USA Today, boosting domain authority.

• Inbound leads for that client grew from one every two months to five per month within two months.

Find out where your brand appears in AI answers today →

Frequently Asked Questions

What is the simplest definition of retrieval-augmented generation?

Retrieval-augmented generation (RAG) is a technique where a large language model retrieves relevant external documents at query time and uses them as context to generate a grounded, sourced answer. Instead of relying purely on training data, the model reads fresh, retrieved content before it responds.

Is RAG the same as how ChatGPT browses the web?

Web browsing in ChatGPT is one implementation of RAG. The model fetches current pages and uses them to answer. Perplexity, Google AI Overview, and Gemini use similar retrieve-then-generate pipelines, each with different source weighting and index preferences.

Why does my Google ranking not automatically translate into AI search visibility?

Google ranks pages by backlinks, authority, and keyword match. LLMs retrieve by semantic relevance, source trust, and structured extractability. A page optimised only for Google may not be formatted in a way an LLM will cite, and it may not appear on the platforms (LinkedIn, Reddit, industry publications) those models preferentially retrieve from.

How do I know which LLMs are mentioning my brand right now?

An AI search audit runs your category's key buyer prompts across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview and records which brands, sources, and documents appear. Simaia's audit covers 50 prompts across those five models and maps exactly where your brand appears versus competitors.

Can RAG visibility be measured?

Yes. Metrics include AI referral traffic (visible in Google Search Console and analytics as bot visits and referral sources), citation frequency across models, and share of AI-answered queries in a niche. Simaia's Healthcare SaaS client moved from 0% to 45% share of AI-driven niche traffic in 2.5 months.

Does optimising for RAG hurt existing SEO performance?

Not if content volume is paced against Google Search Console health. Publishing large volumes of thin or duplicate content can harm organic rankings. Simaia indexes content output against existing Search Console signals so new LLM-targeted content does not cannibalise current Google performance.

What does a company need to do to become a cited source in LLM answers?

Four things: publish structured, factual content formatted for extraction rather than keyword density; place content on the platforms each LLM weights (LinkedIn, Reddit, editorial media); earn domain authority signals that retrieval systems trust; and maintain enough content volume that the model encounters the brand across multiple sources. Done-for-you delivery of all four is Simaia's core service.

About Simaia

Simaia is an agentic marketing team built for B2B companies that want to be found by buyers using ChatGPT, Gemini, Claude, Perplexity, and Google AI Overview. Simaia serves founders, sales leaders, and marketers across APAC, including SMEs, tech startups, outsourcing and HR firms, manufacturers, and service businesses. It delivers strategy, content, distribution, lead identification, and reporting as a done-for-you service, replacing the need to hire a marketing manager, content writer, PR contact, SEO consultant, and lead intelligence vendor separately.