AI Search Algorithms Decoded: What ChatGPT and Perplexity Really Want from Your Content

Ever wonder how an AI like ChatGPT decides which information to include (and cite) in its answers? Or why Bing Chat chooses one article’s snippet over another’s? To optimize for AI-driven search, it helps to understand the algorithms and preferences guiding these models.

In this blog, we’ll demystify how AI search algorithms evaluate content. We’ll look at key factors that ChatGPT, Bing (with its GPT-4 integration), and answer engines like Perplexity.ai prioritize – such as content quality and clarity, authority and citations, structured formats, and more.

By decoding “what AI wants” from your content, you can align your SEO and content creation to those expectations. Consider this an insider’s guide to making your content the kind that AI loves to feature.

Inside the Black Box (as much as we can)

We don’t have the exact code of these proprietary models, but through documentation, behavior observation, and some statements by their creators, we can infer a lot:

Large Language Models (LLMs) and Training Data

  • Models like GPT-4 (which powers ChatGPT and a variant in Bing Chat) are trained on huge datasets (web pages, books, etc.).

  • They’ve internalized a lot of common knowledge and phrasing. If many good answers on the web tend to start with a definition or include certain key points, the LLM will replicate that style.

  • They care about contextual relevance – they understand synonyms, related concepts. So they match query intent with content not by exact keyword but by semantic relevance.

  • They don’t “know” current info unless connected to a search or given up-to-date data. ChatGPT’s base training cuts off in 2021. For latest info, they rely on retrieval plugins or integrations (like Bing).

Retrieval-Augmented Models (Bing, Perplexity)

  • These use search and then feed relevant documents to the LLM to craft an answer (with citations).

  • Bing documentation indicates it looks for content that appears authoritative and relevant. It favors Wikipedia, official sites, quality news, etc.

  • Uberall noted that LLM-based search demands “strong citations, positive reviews on ingestible sources, and presence in trusted listicles or forums” 29.

  • Perplexity.ai explicitly cites multiple sources for transparency. It tends to use Wikipedia, major news, and Q&A sites that directly answer the question.

Quality and Clarity: What AI Looks For

  • Accurate and Consistent – AI cross-checks facts. Contradictions may lead it to skip or caveat your content.

  • Comprehensive yet Concise – Arc Intermedia found AI often used a single source that provided a full answer 53.

  • Well-Structured – Lists, steps, and tables are easy pickings for AI snippets.

  • Human-like but Professional Tone – Conversational and clear content can be reused nearly verbatim by AI, unlike overly salesy text.

  • Citations and Trustworthiness – AI favors content from reliable domains and fact-checked articles. Google SGE, for instance, often cites Mayo Clinic for medical queries.

  • Recency and Freshness – Bing and Google SGE factor in freshness. Updated articles are more likely to be cited for time-sensitive queries.

User Interaction Signals

While ChatGPT itself doesn’t use clicks, Bing and Google factor in engagement:

  • Google’s Sundar Pichai mentioned that content featured in AI overviews gets more clicks if it’s good 73.

  • If one of the citations in Bing consistently gets the most clicks, the system may favor that site more.

This creates a feedback loop: better engagement → more citations.

Adapting to What AI Wants

  • Feed the AI Key Info Upfront – Definitions, stats, and summaries should appear early.

  • Use the AI’s Vocabulary – Include common synonyms and colloquial phrasing. FAQs can help.

  • Multi-Source Affirmation – Publish unique insights across multiple channels (site, PR, Wikipedia).

  • Structured Data & Accessibility – Use schema markup (FAQ, HowTo). Make sure content is crawlable.

  • Avoid Over-Optimization – Don’t spam keywords. Balanced, informative tone is more AI-friendly.

Conclusion

At its core, what AI search algorithms want is very aligned with what good SEO and content marketing have always preached:

  • Be relevant.

  • Be authoritative.

  • Be clear.

  • Be user-focused.

The difference is, instead of optimizing for a search ranking algorithm, you’re optimizing for a reader that is an AI before it becomes a human reader.

By understanding that ChatGPT synthesizes from training data, Bing relies on search + GPT, and Perplexity values transparent citations, you can foresee how your content might be processed and optimize accordingly.

We now have to SEO for two audiences – the AI and the human – but the overlap is significant.

The better you make content for the AI (with clarity and structure), the better it usually is for the user too.