Introduction
Google FastSearch is a proprietary Google technology used to ground Gemini models; it is faster than Google Search because it retrieves fewer documents, trading off some result quality.
What is Google FastSearch
Quick definition: Google FastSearch generates abbreviated, ranked web results that a model can use to produce grounded responses.
Context
The reviewed court document notes that Google employs FastSearch to ground Gemini models. FastSearch relies on RankEmbed signals — a set of search ranking features — and returns compact, ranked results that feed model grounding.
Why this matters
By returning fewer documents, FastSearch lowers retrieval latency and accelerates generation pipelines, but it may deliver lower-quality grounding than a full Google Search ranking.
"To ground its Gemini models, Google uses a proprietary technology called FastSearch. FastSearch is based on RankEmbed signals — a set of search ranking signals-and generates abbreviated, ranked web results that a model can use to produce a grounded response. FastSearch delivers results more quickly than Search because it retrieves fewer documents, but the resulting quality is lower than Search's fully ranked web results."
Judicial document cited
How it works (approach)
FastSearch applies RankEmbed ranking signals to produce abbreviated, ordered results; by intentionally retrieving fewer documents than Search it optimizes speed and latency for AI grounding pipelines.
Implications for Gemini and Vertex AI
Google uses FastSearch internally to ground Gemini responses. The technology is integrated into Vertex AI so third parties can ground on search-derived information, but Vertex customers do not receive FastSearch-ranked results themselves — they receive information extracted from those results, not the original ranking.
The Problem / Challenge
The trade-off is speed versus coverage: fewer retrieved documents can raise the risk of omissions and reduce factual completeness in answers, especially for high-stakes use cases.
Solutions / Best practices
To offset FastSearch limitations: benchmark response quality, add independent verification steps, and consider hybrid retrieval strategies that expand source coverage when accuracy is essential.
- Compare outputs with full Search results for critical queries
- Implement factuality metrics and human review for sensitive content
- Tune latency-quality thresholds per application needs
Limitations and risks
FastSearch is designed for speed and therefore produces lower-quality ranked results than Search; access is restricted and Google preserves core ranking data within Vertex AI to protect its IP.
Conclusion
Google FastSearch offers a speed-optimized grounding path for Gemini but requires careful handling: evaluate trade-offs, implement verification, and choose retrieval breadth based on accuracy needs.
FAQ
Concise answers to common queries about Google FastSearch and its role in model grounding
- What is Google FastSearch? Google FastSearch is a proprietary retrieval technology that returns abbreviated, ranked web results for grounding Gemini.
- Why is Google FastSearch faster than Google Search? Because it retrieves fewer documents, reducing latency at the cost of full-ranking quality.
- Does Vertex AI expose FastSearch directly? Vertex AI integrates the capability for grounding but does not provide customers with FastSearch-ranked results themselves.
- What are the main risks of using FastSearch for grounding? Key risks include reduced source coverage and potential inaccuracies for sensitive queries.
- How to mitigate FastSearch limitations? Use larger retrieval sets for critical tasks, add verification layers, and evaluate outputs against full Search where needed.