Algorithm
Real-time retrieval
Also known as: live retrieval, real-time web retrieval
Real-time retrieval is when an AI system fetches information at the moment of the query rather than relying only on what the model learned during training. The assistant issues searches or direct fetches, reads the returned content and uses it to ground the answer and its citations. It is what allows AI answers to reflect current prices, news, availability and recently published pages.
What it is
It is a retrieval step that runs live inside the answer pipeline, usually combining a search index lookup with fetches of individual URLs. The system then selects passages, passes them to the model as context and produces an answer that points back to the sources used. It contrasts with answers drawn purely from model weights, which reflect a training cut-off.
Why it matters
Real-time retrieval is the main route by which your pages can appear in an AI answer soon after publication. It also means technical accessibility matters again, because a page that cannot be fetched and parsed quickly will not be quoted, no matter how good it is. For time sensitive queries, freshness of the retrieved source often decides which brand gets named.
How it works
Practitioners make content easy to fetch and easy to lift: clear headings, self-contained answers near the top, factual statements with dates, and key information in the server rendered HTML rather than behind scripts or logins. They keep crawler and agent access open in robots rules, keep response times low, and publish structured data so passages are unambiguous. They then monitor whether recent pages are being cited for the queries they target, and test the same prompts over time.
When it applies
It applies to queries where the answer changes: news, pricing, stock, rankings, events, product specifications, regulations and anything the model could not have known at training time.
Examples
- An assistant checks a retailer's live product page before stating whether an item is in stock.
- A query about a rule change published last week returns an answer citing the regulator's page from that week.
- A travel assistant fetches two airline pages at query time to compare current baggage allowances.
How it is measured
- Share of AI answers citing pages published in the last 30 days for your tracked prompts
- Time from publication to first observed citation in an AI answer
- Fetch success rate and response time for AI agents hitting your pages, taken from server logs
- Proportion of your target prompts where the assistant browses at all, rather than answering from memory
Insights on Real-time retrieval
Related terms in Algorithm
- Core updateA broad Google algorithm update affecting search rankings sitewide rather than a single feature. Recent core updates (e.g. May 2026) have increasingly favoured intent-aligned content over keyword-optimised content.
- EEATGoogle's quality framework: Experience, Expertise, Authoritativeness, Trustworthiness. EEAT signals are increasingly weighted in both classical ranking and AI Overview source selection.
- Entity salienceEntity salience is a measure of how central an entity is to a piece of content, as opposed to merely being mentioned in it. Natural language processing systems assign a salience score based on signals such as position, frequency, grammatical role and surrounding context. It helps a system decide what a document is actually about.
- Google Search algorithmThe Google Search algorithm is the collection of ranking and retrieval systems Google uses to find, order and present results for a query. It is not a single formula but many interacting systems that assess content, links, query intent, context and user signals. Google updates these systems continuously, with larger changes announced as core updates.
- Google Spam UpdateA Google Spam Update is a rollout of changes to the systems Google uses to detect and demote content that breaks its spam policies. These updates target things like scaled content abuse, cloaking, hacked pages, link schemes and site reputation abuse rather than general content quality. Sites hit by a spam update usually need to remove the offending practices before they can recover.
- Helpful Content UpdateGoogle's quality-focused update class targeting low-value, AI-generated, or thin content. Successive HCUs have favoured first-hand experience and named expertise over comprehensive but shallow coverage.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
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