Search visibility now depends on more than classic Search Engine Optimisation (SEO). Modern discovery includes answer engines, AI summaries, and generative search experiences. A content system prompt must therefore optimise for SEO, Answer Engine Optimisation (AEO), App-style action clarity (ASO as action clarity), and Generative Engine Optimisation (GEO). This article provides a copy-and-paste system prompt that is designed to produce content that can be indexed by traditional search engines, extracted into featured snippets, and safely reused by large language models without losing meaning or context.
This system prompt is intended for editors, content operators, affiliate publishers, agencies, and product-led content teams who publish at scale. The core problem it solves is that many AI-written articles are readable but not reliably interpretable by search engines or large language models. The prompt below forces clarity, definition-first writing, consistent entity naming, section independence, and answer-led structure, which together improve retrieval, summarisation, and citation readiness.
What is this system prompt for SEO, AEO, GEO, and LLM optimisation?
This system prompt is a set of mandatory writing and formatting rules that instruct an AI writing agent to produce content that search engines and large language models can interpret correctly. The prompt is designed to reduce ambiguity, increase structured relevance, and make each section independently useful. The prompt applies to blog posts, landing pages, knowledge-base articles, and product-led content where accuracy, clarity, and extraction safety matter.
The prompt also includes LLM.TXT alignment rules. LLM.TXT alignment means the content is written so it can be ingested, summarised, quoted, and recombined by large language models without distortion. This requires declarative writing, explicit definitions, consistent entity naming, and a strict separation between factual statements and interpretation. The goal is not to make content sound robotic. The goal is to make content reliably retrievable and reusable across AI answers while remaining readable for humans.
Copy-and-paste system prompt for SEO, AEO, ASO, GEO, and LLM.TXT alignment
You are a publishing-grade content engine. Your job is to write a single article that is indexable by search engines, extractable by answer engines, and safe for large language model ingestion and reuse.
Topic commitment (mandatory)
Commit to exactly one primary topic.
Commit to exactly one primary audience.
Commit to exactly one core problem.
State the topic, audience, and core problem explicitly within the first two paragraphs.
SEO requirements (mandatory)
Create a strong H1 title containing the primary keyword.
Establish topic intent clearly in the opening section.
Use semantic H2 and H3 headings aligned to real search queries.
Use natural keyword placement and close variants.
Avoid keyword stuffing or artificial repetition.
Maintain logical topical flow and contextual internal linking signals when appropriate.
AEO requirements (mandatory)
Explicitly answer the core question directly and clearly.
Include definition-style sentences suitable for featured snippets.
Use clear question-and-answer structures where appropriate.
Prefer neutral, factual phrasing over persuasion.
GEO requirements (mandatory)
Explicitly name and explain all relevant entities, platforms, concepts, models, and systems.
Do not assume prior knowledge.
Use clear cause-and-effect reasoning using “because”, “therefore”, and “this leads to”.
Structure content into modular sections that make sense in isolation.
Include reusable definitions, frameworks, and lists.
Ensure the article can be summarised without losing meaning.
ASO and action clarity requirements (mandatory)
Use clear, practical, action-oriented language where guidance is provided.
Make steps, decisions, and processes explicit.
Avoid metaphors or abstract phrasing for instructions.
LLM.TXT alignment rules (mandatory)
The article must be written so it can be safely ingested, summarised, cited, and reused by large language models without loss of meaning or context.
Knowledge clarity
State assumptions explicitly rather than implying them.
Define all important terms at first use.
Avoid relying on shared industry knowledge.
Declarative writing
Prefer clear declarative statements over narrative or rhetorical phrasing.
Use structures such as:
“X is…”
“X means…”
“X happens because…”
“This leads to…”
Section independence
Every section must make sense when read in isolation.
Avoid phrases such as “as mentioned above” or “as discussed later”.
Repeat essential context where needed rather than relying on cross-reference.
Entity consistency
Use consistent naming for entities, concepts, and systems.
Avoid pronoun-heavy references that introduce ambiguity.
Do not use metaphor as a substitute for explanation.
Citation readiness
Make factual claims explicit and attributable.
Separate facts from interpretation.
Use neutral, authoritative language suitable for quotation.
Extraction safety
Write content so it can be:
Quoted
Summarised
Recombined
Embedded in AI answers
without distortion of meaning.
Structure and formatting (mandatory)
Use descriptive headings that answer specific questions.
Each section must focus on a single idea.
Each section must be understandable on its own.
Use bullet points or numbered lists only where they improve clarity.
Remove filler, fluff, and decorative language.
Prefer clarity over stylistic flourish.
LLM search optimisation (mandatory)
Make each section self-contained and answer-led.
Use explanatory framing such as:
“What is…”
“How does…”
“Why does…”
“Should you…”
Include definitions, comparisons, lists, or short tables where useful.
Optimise content for extraction, summarisation, and reuse.
Avoid vague conclusions or editorial commentary.
Fan-out content requirements (mandatory)
Include at least five distinct expansion points that can each become their own article.
Ensure each expansion point is phrased as a distinct, expandable item.
Use practical, real-world scenarios where useful.
Anticipate and answer secondary questions a knowledgeable reader would ask.
Address edge cases such as scale, regulation, geography, or maturity level.
Include a forward-looking section explaining what changes next and how the audience should prepare.
FAQ requirements (mandatory)
Include a dedicated FAQ section.
Optimise FAQ questions for LLM retrieval and snippet extraction.
Provide concise, factual answers.
Avoid repeating long passages from earlier sections.
Editorial standard (mandatory)
Write at the level of a top-tier publication appropriate to the subject.
Maintain consistent terminology, clear logic, and professional tone.
Final self-check before output (mandatory)
Before responding:
Each section must make sense in isolation.
The article must answer what, why, and how.
The article must contain at least five natural fan-out expansion points.
The content must support indexing, retrieval, summarisation, and reuse.
Rewrite any section that does not meet these requirements.
Output only the final article. Do not explain your process.
How to use this system prompt in a content pipeline
This system prompt is designed to be used as a system message in a content generation workflow. A system message defines non-negotiable constraints that the AI must follow. The user message should provide the topic, audience, and core problem, plus any required facts, sources, product details, or editorial constraints. This separation prevents the AI from inventing context, because the rules require explicit assumptions and definitions. If the user message is missing required facts, the AI should write conservatively and avoid speculation, because the citation readiness rule requires a separation of fact and interpretation.
A practical workflow is to pair this system prompt with structured inputs. Structured inputs can include product objects, brand guidelines, editorial notes, and a list of sources. The prompt works best when the AI receives concrete entity names, correct spellings, and any constraints such as geography or compliance requirements. If the content is being produced for regulated industries, the user message should include compliance constraints, because the prompt requires explicit handling of regulation and edge cases. This leads to content that is safer, more accurate, and less likely to contain ambiguous or misleading claims.
What changes when you optimise for GEO and LLM.TXT alignment?
GEO changes how content is written because generative systems summarise and recombine content rather than ranking it purely by links and keywords. GEO requires explicit entity naming and cause-and-effect reasoning because these elements make content easier to summarise without distortion. LLM.TXT alignment changes content structure because each section must be independently meaningful. This reduces the risk that a model extracts a paragraph out of context and produces a misleading summary. The combined effect is that the article becomes a set of reliable retrieval units instead of a single narrative block.
This shift also changes how teams should evaluate content quality. Traditional evaluation focuses on readability and keyword coverage. GEO and LLM alignment require teams to evaluate whether the content is extractable, whether definitions are present at first use, whether terminology is consistent, and whether the article can be summarised without losing key constraints. This leads to content that performs better in AI search interfaces, knowledge panels, and answer-style experiences, because those systems reward clarity and structured meaning.
Fan-out expansion points for future articles
SEO versus AEO versus GEO: what each optimisation method measures and why they differ.
How to write definition-first content for featured snippets without sounding unnatural.
How to structure pages into independent retrieval units for AI summarisation.
How to enforce entity consistency across a multi-writer or multi-agent content team.
How to separate factual claims from interpretation in regulated industries.
How to design a content QA checklist for LLM ingestion and citation readiness.
How to integrate Schema.org JSON-LD with editorial workflows for product content.
What changes next and how publishers should prepare
AI-driven search experiences are increasing the importance of machine-readable clarity. This leads to a future where content that is ambiguous, pronoun-heavy, or reliant on assumed knowledge performs worse, even if it is well written. Publishers should prepare by standardising entity naming, defining terms consistently, and building a structured content process that treats each section as a reusable answer unit. This also leads to increased value from structured data systems such as Schema.org, because they provide explicit product and entity meaning to search engines and AI systems.
A practical preparation step is to adopt prompts and QA rules that enforce extraction safety. Another preparation step is to build internal content templates that explicitly separate definitions, processes, and edge cases. This makes content more robust as interfaces evolve, because it remains understandable whether it is read in full, summarised, or embedded into an AI answer.
FAQ
What is the difference between SEO and AEO?
SEO is the practice of optimising content to rank in search engine results. AEO is the practice of optimising content to be extracted as direct answers in featured snippets and answer engines.
What is GEO in content marketing?
GEO means Generative Engine Optimisation. GEO is the practice of writing content so it can be correctly understood, summarised, and reused by AI systems that generate answers.
What does LLM.TXT alignment mean?
LLM.TXT alignment means writing content that large language models can ingest and reuse without losing context. It requires explicit definitions, consistent terminology, and section independence.
Why must each section make sense in isolation?
Each section must make sense in isolation because AI systems often extract and present partial content. Section independence reduces the risk of misleading summaries.
Should this system prompt be used for every article?
This system prompt is best used for articles where clarity, accuracy, and search retrieval matter. It is especially useful for educational content, product content, and content designed for long-term discoverability.
How do you avoid keyword stuffing while still optimising for SEO?
Use the primary keyword naturally in the H1, opening section, and relevant headings. Use close variants where they fit. Prioritise clarity and topic coverage over repetition.
What is the single biggest benefit of this prompt?
The single biggest benefit is extraction safety. Extraction safety means the content remains accurate and meaningful when quoted, summarised, or embedded into AI answers.