1 Multi-layer architecture instead of a "single algorithm"
Google relies on a cascade of dozens of modules. Each module is responsible for a separate stage—from document retrieval to the final sorting and SERP generation. This design lets engineers update individual blocks quickly without putting the entire system at risk.
2 Google’s main systems and their signals
| System | What it measures | Data source | Key features |
|---|---|---|---|
| T* Topicality | Relevance | Content, anchor text, clicks | Hand-crafted formulas, easy to debug |
| Navboost / Glue | Behavioral data | 13 months of aggregated clicks | Not an ML model but a large table of click frequencies |
| Q* Quality Score | Trust and quality | PageRank, distance from authority sites | Baseline “shield” against spam |
| RankEmbed | Semantic proximity | LLM encoder, one month of search data | Fast ranking for high-volume queries |
| RankBrain | Query reinterpretation | Clicks and assessor ratings | Re-orders the top 20–30 results, CPU-intensive |
3 Content freshness and Instant Glue
For highly time-sensitive topics—news, finance, sports—Freshness Node and Instant Glue kick in. The system collects user actions from the last 24 hours and promotes newly published material above older pages.
4 Links are still foundational
- Both the quantity and, more importantly, the quality of backlinks are considered.
- Thematic relevance of the source, link position, and natural anchor text all matter.
- High-quality links boost Q* and speed up indexing.
5 Twiddlers and manual fine-tuning
After the main ranking phase, results pass through Twiddlers—small modules that add or subtract weight in real time for specific situations: combating content farms, balancing localization, removing CTR manipulation, and more.
6 Striking a balance: relevance, trust, experience
- Relevance — T* and RankEmbed
- Trust — Q* and PageRank
- Freshness — Instant Glue and Freshness Node
- User behavior — Navboost and Chrome metrics
7 The role of LLMs and the future of search
- AI Overviews are built via Retrieval-Augmented Generation, with the model using top results as context.
- RankEmbed shows how LLM encoders already shorten response times, but a full shift to end-to-end AI remains costly and harder to debug.
What SEO specialists should do in 2025 with Google and Bing
- Publish useful content—the words on the page are still the strongest signal.
- Optimize for engagement—retain attention and improve CTR.
- Build trust—authoritative links, transparent structure, technical cleanliness.
- Update and expand materials to qualify for Freshness Node boosts.
- Structure data and make content “chunkable” for AI Overviews.
Google keeps getting more complex, but the principle remains: create high-quality, up-to-date, well-sourced content that satisfies user intent better than anyone else.