The way people find information is changing. Users are shifting from browsing search results to acting on AI-generated answers directly - often from just a handful of sources. Traffic from these systems converts at significantly higher rates than traditional search. The implication is structural: visibility is consolidating around fewer sources, and each citation carries more weight.

And since traffic from LLM-based systems converts at 9x the rate of traditional search, being selected has become crucial: If your organization isn't being cited by AI engines, it risks becoming invisible.

At Linkup, we build retrieval infrastructure used by AI systems like ChatGPT, Perplexity, and Claude to access external content. We handle the full lifecycle – crawling, ingestion, indexing, filtering, and serving content for answer generation.  We see firsthand what it takes for content to be recognized, retrieved, and reused by AI systems.

This guide explains the mechanics behind AI-driven discovery and how to optimize for it. The core principles:

How AI systems search for content

AI systems pull external content through multiple mechanisms, often combined in the same pipeline.

Traditional search engines still play a role. This means classic SEO factors matter for getting into the candidate set. But even when traditional search is the entry point, pages are fetched and processed further before being passed to the language model for selection. This means page content needs to be optimized for LLM processing.

Specialized retrieval APIs are increasingly common. These systems combine semantic similarity, keyword matching, authority signals, and structural cues – moving beyond pure keyword ranking. Linkup is one example. We enable AI systems to retrieve content at the fragment level, pulling specific sections or data points rather than whole pages.

Internal indexes maintained by AI providers also feed the pipeline. These are populated through crawling, licensing deals, and partnerships, and they're optimized for fragment-level retrieval with emphasis on semantic relevance, freshness, and structure rather than link-based authority.

This is where Generative Engine Optimization (GEO) comes in. SEO focuses on getting pages discovered by humans and ranked as links. GEO optimizes content so generative AI models can select, retrieve, and reuse it at the fragment level

Across all these mechanisms, semantic similarity and content clarity are becoming the dominant signals.

How Semantic Retrieval Works

While implementations vary, most systems follow a similar process:

1. Ingestion. Content is crawled from websites and split into fragments – paragraphs, list items, table rows, sections. These fragments, not full pages, become the units that compete for retrieval.

2. Embedding. Each fragment is converted into a vector: a numerical representation of its meaning. These vectors are stored in a database optimized for similarity search. Content with similar meaning produces vectors that are mathematically close, even when the wording differs.

3. Retrieval. When a user asks a question, that query is also converted into a vector. The system finds fragments whose vectors are closest to the query vector, then filters them using signals like freshness, authority, and redundancy. Only a small number make it through.

4. Generation. The selected fragments are passed to the language model, which synthesizes them into a final answer – summarizing, comparing, combining. The user sees a single response in natural language, often with few or no visible sources.

Each of these steps represents an opportunity for you to optimize your content to be selected in the response.

What This Means in Practice

The retrieval model above has direct implications for how content should be published. We think about it in three layers: making content easy to ingest, easy to chunk and embed, and easy to rank and select.

Make Content Easy to Ingest

If content can't be crawled and parsed reliably, it gets excluded before semantic retrieval even begins.

1. Crawl access. Content must be accessible without authentication. Avoid heavy client-side rendering that breaks crawlers. Ensure robots.txt and meta tags allow access by major search and AI crawlers.

2. Parseable formats. Deliver content as clean HTML. Don't bury essential information in images, PDFs, or interactive components that can't be parsed. Use semantic elements – headings, lists, tables – to expose structure.

3. Stable URLs. Use canonical, permanent URLs. Avoid session-based or frequently changing paths. Each piece of content should have one authoritative location.

Make Content Easy to Chunk and Embed

Once ingested, content is split into fragments and embedded. Retrieval operates on these fragments independently, which means each one needs to work on its own.

1. Lead with the conclusion. Start each section with a clear statement of the key point, then provide explanation and evidence. The opening sentences of a section generate the embeddings most likely to be retrieved – buried answers are harder to find.

2. Clear titles and permalinks. Use descriptive headings that reflect the actual content. "How pricing works" beats "Overview." Generic headers hurt retrieval. Add anchors like #pricing or #setup so AI systems and users can reference specific chunks directly.

3. Independent sections. Each section should answer a specific question without requiring context from elsewhere on the page. Avoid "as mentioned above" or assumptions about what the reader already knows. A section retrieved in isolation should still make sense.

Also, make sure your explanations are direct and avoid language that feels like marketing storytelling. Use concrete numbers and definitions as much as possible.

Content that embeds cleanly and produces self-contained vectors gets retrieved more consistently.

Make Content Easy to Rank and Select

After retrieval, fragments are filtered and ranked. Only a small subset makes it into the final answer. To avoid being filtered out, content needs to signal reliability.

Maintenance. Keep content current. Mark updates explicitly. Stale content gets deprioritized even when it's relevant.

1. Structured presentation. Tables, lists, and comparisons are easier to parse, rank, and reuse than dense prose. Use them for key facts.

2. Answer-oriented structure. Frame sections around questions users are likely to ask, then answer those questions directly. FAQ sections work well because they create distinct embeddings that can match a wider range of queries.

3. Ownership, attribution, and licensability. Clearly identify the author or organization. Include credentials where relevant. Anonymous or unattributed content is treated as lower confidence. Make content ownership and reuse rights explicit. Systems with conservative filtering may exclude content with unclear rights.

One additional note: LLMs tend to skip over information they already "know" from training. If you have genuinely new data, original research, or unique insight, that content becomes more valuable because the model can't generate it on its own.

How Different Engines Decide What to Surface

The principles above are broadly shared, but AI engines differ in how they weight signals like freshness, attribution, authority, and synthesis style. What works on one platform may be invisible on another.

In an analysis of 100,000 prompts run across both ChatGPT and Perplexity, only 11% of cited domains appeared in both. That overlap shrinks further as you add more platforms.

Success means identifying where your audience asks questions, optimizing for that engine's retrieval behavior first, then expanding coverage.

Conclusion

GEO isn't a replacement for SEO – it's an additional layer, aligned with how AI systems actually retrieve and use content. The organizations that adapt early will compound their advantage as AI-mediated discovery becomes the norm.

At Linkup, we power the retrieval infrastructure behind ChatGPT, Perplexity, and Claude.

Want to learn more? Reach out, or get started here.