SEO in LLMs How to gain visibility in generative answers?

Search engines are no longer the only discovery “filters”. Today, end users and professionals are directly querying language models (LLMs) such as ChatGPT, Perplexity or Gemini; and even assistants embedded in browsers, CRM or SaaS. The consequence? A new frontier emerges: positioning your brand so that LLMs cite you, use you as a source and recommend you. At Inprofit we call it SEO in LLMs and, if you want to compete for the “top of answer”, you need a different strategy than classic SEO.

Below, we share an advanced operational framework, actionable tactics and measurement criteria for your company to lead the conversation in generative responses.

What is really SEO in LLMs (and what is not)?

The SEO in LLMs is the set of practices to increase the probability that a model will select you as evidence, include you in its context and name you in the response. It does not replace traditional SEO; it complements it with a focus on:

• Extreme machine-readability: structured, disambiguated and easy-to-cite content.
• Evidentiability: clear evidence, verifiable sources and traceability.
• Semantic coverage: encompassing tasks, questions and use cases (not only keywords).
• Operational freshness: frequent changes and real update signals.
• Functional authority: not just links; datasets, calculators, step-by-step guides, repositories, libraries and usable documentation.

It’s not about “tricking” the model with prompts or term density. It is about being the best source for the model, with technical and editorial standards designed for AI consumption.

How do LLMs “read” the web?

Understanding the web/ecommerce flow helps you optimize:

1. Discovery: traditional and/or wizard-owned crawlers locate URLs.
2. Parsing and chunking: the content is segmented into fragments (passages) of approximately 200-1,000 words.
3. Semantic indexing: embeddings and metadata are generated.
4. RAG / Grounding: when responding, the model searches for relevant passages, reorders them and injects them as context.
5. Citation / attribution: depending on the attendee, the answer will include visible references or not.

Your mission with SEO in LLMs: maximize the eligibility of your passages in steps 2-4 and facilitate attribution in step 5.

Agency framework for SEO in LLMs

1) Content architecture “LLM-first”.

• Design by task: create “how to do X” hubs with numbered steps, expected inputs/outputs and frequent errors.
• Include explicit Q&A: add sections of direct questions with concrete answers of 2-4 sentences.
• Summarize above, drill down below: an initial executive summary + detailed technical sections.
• Reproducible examples: snippets, downloadable datasets, calculators, templates. LLMs “like” the demonstrable.

Do you want an LLM-first editorial audit? At Inprofit we design the content map and deliver a prioritized backlog in 2 weeks.

2) Technical structure and signaling

• Comprehensive Schema.org (Article, HowTo, TechArticle, FAQPage, Product, Dataset, SoftwareSourceCode).
• Marking of steps (HowToStep) with tools, times and expected results.
• Tables and definitions: glossaries with short definitions and variants of terms for disambiguation.
• Stable IDs per section: hash links and titles with consistent nomenclature to make passages linkable.

3) Eligible passages and “citable blocks”.

Converts each heading into a quotable block:

• Paragraphs of 2-4 lines, one idea per paragraph.
• A “claim” phrase followed by an evidence or example.
• Where applicable, round figures and ranges (avoid vagueness).
• Operational conclusion: “What to do now” in 1-2 lines.

4) Functional authority (beyond link building)

The authority that an LLM values is not just social; it is enforceable utility:

• Repositories (GitHub/Bitbucket) with clear licenses.
• APIs and public documentation.
• Datasets with diagrams and examples.
• Benchmarks and replicable methodologies.
• Real cases with metrics (even if they are ranges) and learnings.

We help you convert your internal assets into publishable functional authority (datasets, code, docs).

5) Freshness and maintenance signals

• Changelogs visible per page: date and “what changed”.
• Guide versioning (v1.2, v1.3…).
• JSON/Atom feeds for trackers to detect news.
• Machine-readable dates (ISO 8601) and sitemap with real lastmod.

6) Programmatic semantic coverage

Map real tasks that users ask an assistant:

• “How to calculate…”, “template for…”, “example of…”, “steps to…”, “errors when…”.
• Multiply variations by audience (SME, enterprise, marketing, sales) and by context (Spain/LatAm, regulations, tech stack).
• Generate clean URL paths aligned with those tasks.
• Create collections (series) that an LLM can understand as a complete guide.

Specific on-page tactics for SEO in LLMs

Discover the most important points of On-page SEO. Some of the international tactics are:

Machine-readable content

• Informative headings: avoid cryptic titles; use “Verb + Object + Condition” syntax.
• Short definitions at the beginning of each section (“In one sentence: …”).
• Tables with parameters and default values.
• Micro-summaries at the end of each H2 with 2-3 bullets (optional, one per page).

Evidence and sources

• Where you quote, link directly to the original data.
• If there is no public source, state methodology and provide downloadable raw data.
• Include screenshots or diagrams with descriptive captions (captions help parsing).

Fragment optimization (conscious chunking)

Although you do not control the chunking of the indexer, you can suggest it:

• Short sections (300-500 words) with meaningful subtitles.
• Numbered lists for procedures (without abuse).
• Code blocks and separate quotes: delimit useful segments.

Task-oriented interlinking

• Links between consecutive steps and to previous concepts (glossary).
• Use precise anchor text (“set up GA4 events”, not “here”).
• Create bridge pages: “From theory to practice” with compilation of tools.

Do you need to restructure your internal architecture? Redesign interlinking with a focus on tasks and measure its effect on attendee acquisition.

Off-page for LLMs: signals that weigh

• Citations in third-party documentation: integrate your guide in READMEs of tools, technical forums and academies.
• Participation in issues and pull requests: the footprint in public repos is a citable asset.
• Collaborations with universities or communities: whitepapers and shared notebooks.
• Events and webinars with downloadable materials and indexable transcripts.

Optimization-specific KPIs

Measuring attendee visibility requires new metrics:

1. Task coverage: number of intents (task-questions) for which you have a citable block.
2. Recall in answers: percentage of test prompts where the wizard selects you as the source.
3. Visible attribution: rate of responses showing your URL/name.
4. Time to update: hours/days from the time you change a page until the updated version appears in responses.
5. AI-assisted conversion: contacts or downloads originating from an interaction with assistants (tracking through dedicated landings and UTM codes).

How to organize it quickly?

• Build a bank of prompts per vertical (100-300 questions) and evaluate weekly.
• Use LLM-as-judge method to rank your responses vs. competitors and detect coverage gaps.
• Implement “from wizard” landings with specific messages, offers and UTMs to attribute channel.

Technical checklist (for marketing and dev teams)

1. Correct and validated Schema.org (HowTo/FAQ/Article/TechArticle/SoftwareSourceCode/Dataset).
2. Sitemaps: standard + video/image + lastmod real.
3. Changelogs and URL versioning.
4. Glossary with brief and unambiguous definitions.
5. Tactical Q&A on each key piece.
6. Citable blocks: claim + evidence + operational conclusion.
7. Functional assets (code, dataset, calculator).
8. Task-oriented and next-step oriented interlinking.
9. Feeds (Atom/JSON) to facilitate tracker subscription.
10. Bank of prompts and KPIs panel of the “assistants” channel.

Do you want the checklist as an editable template and a monitoring dashboard?

Common mistakes when positioning in AI

• Think only in keywords and not in task intents.
• Encyclopedic content without utility: lots of text, little action.
• False dates or changes without changelog: models end up discarding you.
• Hide the methodology: if you cannot be audited, you are less likely to be cited.
• Duplicate without canonicals or consolidation: confuses the sampling of passages.

30-day implementation roadmap

Week 1

• Content audit: identify 10-15 URLs with LLM potential.
• Define master glossary and taxonomy by task.
• Design a template of a quotable block.

Week 2

• LLM-first rewrite of 5 key URLs with schema + changelog.
• Publication of a functional dataset/template.
• Creation of the prompts bank.

Week 3

• Interlinking by tasks and bridge pages.
• Recall tests on assistants and on-page adjustments.
• Activation of specific feeds and sitemaps.

Week 4

• Launch of 5 new task-oriented pieces with Q&A.
• First KPI report: coverage, recall, attribution, freshness.
• Quarterly programmatic scaling plan.

At Inprofit we execute this roadmap end-to-end: strategy, production, development and measurement. Shall we schedule a session to see your case?

The new “top 1

To compete in SEO in LLMs is to accept that the traditional ranking coexists with a ranking of passages and verifiable usefulness. The winner is the one who facilitates the work of the model: structure, evidence, coverage and continuous updating. If you become the best source for solving tasks -not only for positioning words-, your brand will appear in answers, recommendations and conversational flows.

At Inprofit we are already helping SMEs and companies to build this advantage. If you want to lead the generative responses in your industry, let’s talk and we’ll design a customized plan.