Looking at the clues from publicly available sources, we know there is a multi-stage process where your brand\u2019s content can either get cited, paraphrased, or dropped entirely while an answer is assembled.<\/p>\n\n\n\n
In this guide, we\u2019ll break down those 7 stages, so you can see exactly where and how your brand has a chance to surface.<\/p>\n\n\n\n Part of ChatGPT\u2019s database is frozen in time. It was trained on a big snapshot of the internet that includes sources like Wikipedia, Wikidata, Crunchbase, G2 reviews, and major news outlets. <\/p>\n\n\n\n If your brand was mentioned across the web when this snapshot was taken, you start with a bit of an advantage. The system has \u201cseen\u201d you before, so you\u2019re not a stranger when someone asks about your category.<\/p>\n\n\n\n But if you weren\u2019t, you may not surface quite as easily as companies that were. While you may be at a slight disadvantage, the story isn\u2019t over. Brands will have to focus on meeting ChatGTP\u2019s content preferences to continue being cited, no matter what their web presence looked like when the LLM was being trained initially. <\/p>\n\n\n\n ChatGPT starts every answer with an intent check. The system analyzes what kind of answer fits best:a definition, a comparison, a step-by-step guide, or something else? Once intent is set, the query expands outward, and the system generates a bundle of related questions, which is a process known as query fan-out<\/a>. <\/p>\n\n\n\n A prompt like \u201cbest CRM for startups under $70\u201d<\/em> often branches into variations such as \u201caffordable CRM software,\u201d \u201cCRM with Slack integration under $70,\u201d<\/em> or \u201csmall business CRM comparison.\u201d<\/em> ChatGPT does this so it can cover the different ways people might phrase the same need.<\/p>\n\n\n\n For SaaS brands, this is where breadth pays off. <\/strong>When you have a site built around clusters, with a hub page for the main topic and supporting pages that go into different angles, your brand is better positioned to be retrieved across multiple fan-out variations. You\u2019re essentially giving the system many different entry points to bring your brand into the conversation.<\/p>\n\n\n\n Once ChatGPT has spun out a bundle of related queries, it goes looking for answers. This retrieval stage pulls in material from the public web, and, if a user has connected<\/a> services like Google Drive or Notion, it can also reach into those private sources.<\/p>\n\n\n\n The system doesn\u2019t rely on one type of matching. It mixes straightforward keyword lookups with vector embeddings<\/a> (different types of data – text, audio – expressed in numerical form), which allow it to spot meaning even if the wording isn\u2019t exact (called the semantic search<\/a>). <\/p>\n\n\n\n Filters are applied by source and file type to make sure the right kinds of documents (in-depth, unbiased) are being considered.<\/p>\n\n\n\n Source: GeeksforGeeks<\/a><\/p>\n\n\n\n For you, this is the first real point of influence. If critical information about your product is hidden behind JavaScript, buried in images, or spread across unstable URLs, the system may not even grab it in the first place.<\/strong> By this point, ChatGPT has a pile of results from all of its fan-out queries. The next step is to merge them into one unified list. The method it uses to do that is called Reciprocal Rank Fusion<\/a> (RRF). This process rewards pages showing up consistently across query variations rather than just for one.<\/p>\n\n\n\n Every time a page appears in a search variation, it gets a score. Here\u2019s the formula ChatGPT uses to score a page for every fan-out query:<\/p>\n\n\n\n RRF score = 1 \/ (60 + rank position)<\/strong><\/p>\n\n\n\n That number seems small at first, but the trick is in how it adds up across all the different queries.<\/p>\n\n\n\n Here\u2019s a simple example:<\/p>\n\n\n\n That means if your site is ranking #5 for ten related searches, it will carry more weight than a site ranking #1 for just one. <\/p>\n\n\n\n For SaaS brands, this reinforces the importance of topical depth. When you cover all the natural variations and use cases around a core topic, you\u2019re more likely to rank across many of ChatGPT\u2019s fan-out queries, and those repeated mid-tier rankings add up to more influence than a single #1 result.<\/p>\n\n\n\n\n ChatGPT decides what gets attention, and it’s not always obvious how. Book a quick session with us to help you understand the process so your content gets seen.<\/p>\n \n \n \n TL:DR;<\/strong><\/summary>\n
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How ChatGPT Processes and Ranks Your Content (in 7 Stages)<\/h2>\n\n\n\n
Pretraining data: ChatGPT\u2019s Built-in knowledge <\/h3>\n\n\n\n
Stage 1: Interpreting, Rewriting, and Expanding the Query<\/h3>\n\n\n\n
![\"Query](\"https:\/\/singularity.digital\/wp-content\/uploads\/2025\/12\/USER-QUERY-1024x576.png\")
<\/figure>\n\n\n\nStage 2: Retrieval (web + connected sources)<\/h3>\n\n\n\n
![\"semantic](\"https:\/\/singularity.digital\/wp-content\/uploads\/2025\/12\/image-1.png\")
<\/figure>\n\n\n\n
<\/strong><\/p>\n\n\n\nStage 3: Fusion across multiple searches<\/h3>\n\n\n\n
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![\"ranking](\"https:\/\/singularity.digital\/wp-content\/uploads\/2025\/12\/ranking-1024x576.png\")
<\/figure>\n\n\n\nUnderstand chatgpt rankings <\/h3>\n \n