How to Get Your Content Picked Up by LLMs: A GEO Guide Backed by AI Research

As large language models (LLMs) shape the future of search, influence, and content distribution, a new question emerges:

How do you create content that language models actually learn from - and repeat?

The key to GEO (Generative Engine Optimization) lies in understanding how LLMs select their training data.

A recent paper by Albalak et al. (2024), A Survey on Data Selection for Language Models, breaks down how data is filtered, weighted, and selected for LLM training. It gives us a rare window into how AI “decides” what’s worth learning from.

Here’s your actionable guide to creating content that aligns with those selection principles - and gets picked up by the next generation of LLMs.

1. Publish in the Right Places

LLMs are trained on massive datasets - but not all web pages are included. Much of the web is noisy, spammy, or irrelevant.

✅ Action Steps

• Prioritize publishing on high-trust domains: Think .edu, .org, or .gov. These are more likely to be included or oversampled in LLM pretraining datasets.
• Get cited by reputable sources: If Wikipedia, academic papers, or major news outlets link to you, your domain gains more weight.
• Contribute to canonical public sources: Open source documentation, scientific repositories, and widely mirrored platforms like GitHub and ArXiv often show up in model training sets.

LLMs often train on data sourced from Common Crawl, Wikipedia, ArXiv, and filtered web corpora like The Pile or C4 (Raffel et al., 2020).

2. Write Like High-Quality Training Data

LLMs are trained to minimize loss - they get better at predicting text that’s predictable, structured, and high-quality. So your content should emulate the tone and format of data that helped the model “learn.”

✅ Action Steps

• Use clear, consistent formatting: Headings, bullet points, and clean paragraphs improve readability - and predictability.
• Avoid slang and filler: Informal, rambling, or “fluffy” content is less likely to match the clean, factual tone that training filters favor.
• Use technical language appropriately: Models learn best from content with accurate domain-specific terminology, so don’t oversimplify.

“Quality” in LLM training often includes dimensions like coherence, correctness, educational value, and clarity (Wettig et al., 2024; Liu et al., 2025).

3. Align With Data Selection Criteria

Training pipelines typically apply static and dynamic filters to select data. This includes:

• Deduplication
• Toxicity filtering
• Domain balancing
• Writing quality scoring

You can reverse-engineer these filters to your advantage.

✅ Action Steps

• Don’t plagiarize or repeat boilerplate content: Duplicated text is heavily downweighted or removed entirely.
• Keep language civil and professional: Toxicity filters remove text with offensive or aggressive tone.
• Diversify your examples: Data diversity is often favored, especially in dynamic selection methods like curriculum learning.

Dynamic selection strategies (e.g., GREATS, QuaDMix) show that training efficiency improves when models see varied, high-impact data early on (Albalak et al., 2024).

4. Structure Your Content Like a Textbook

Why? Because textbooks, encyclopedias, and manuals are consistently used in high-quality training data - and they’re easy to learn from.

✅ Action Steps

• Use definitions, examples, and summaries: Mimic the structure of teaching material.
• Repeat key ideas using varied phrasing: Helps reinforce patterns.
• Add diagrams or explainers when applicable: Even though models don't “see” images (yet), text around diagrams often contains high-signal information.

LLMs pick up on pedagogical patterns - questions, answers, definitions - because they occur predictably in training data (Raffel et al., 2020; OpenAI docs).

5. Make Your Content Crawlable

A model can’t learn from your content if it can’t read it.

✅ Action Steps

• Avoid content buried in PDFs or JavaScript: Keep key information in raw HTML or markdown.
• Use alt text and semantic HTML: Increases the chance of being indexed by crawlers.
• Add metadata that reinforces topic clarity: Title tags, structured data, and summaries help match context.

LLMs trained on web data (like Common Crawl) use filters to remove pages that fail parsing or have bad markup.

6. Include Self-Contained, High-Value Segments

Many data selection systems (like QuRating or LESS) now evaluate segments, not whole articles. That means your blog post may only partially be included - so each section needs to stand on its own.

✅ Action Steps

• Start each section with a short, direct takeaway
• Keep paragraphs focused and self-contained
• Write so that someone could quote you out of context - and still getvalue

Token-level quality scoring (Wettig et al., 2024) suggests that even parts of a document may be included or excluded based on their internal consistency and usefulness.

Final Thoughts

The question is no longer how do I get people to read my content anymore - it’s how do I get LLMs to internalize it.

By aligning your writing style, structure, and publishing strategy with how data is selected for model training, you make your content not just accessible to humans - but foundational for machines. And that’s the real goal of GEO after all.

📚 Sources cited

• Albalak et al., A Survey on Data Selection for Language Models, 2024. (https://arxiv.org/abs/2402.16827)

• Wettig et al., QuRating: Selecting High-Quality Data, 2024. (https://arxiv.org/abs/2402.09739)

• Liu et al., QuaDMix: Quality-Diversity Balanced Data Selection, 2025. (https://arxiv.org/abs/2504.16511)

• Raffel et al., Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, 2020. (https://arxiv.org/abs/1910.10683)