Gemini Grounding with Google Search

What this prompt does

This prompt implements Gemini grounding with Google Search so responses stay factual and current with real source citations. It covers six steps: configuring search grounding with a dynamic retrieval threshold, applying grounding-aware prompt engineering, parsing and displaying grounding metadata, building a grounding-quality evaluation pipeline, adding caching and cost optimization, and creating a grounded-vs-ungrounded comparison test. Tuning the threshold per use case and rendering real citations is what makes the output trustworthy rather than confidently wrong.

The variables tailor grounding to your app. [retrieval_threshold] controls how often the model grounds, [use_case_count] and [use_cases] set where grounding applies, and [topic_area] plus [time_window] shape the prompts. [analytics_store] and [quality_threshold] drive evaluation, [news_ttl], [reference_ttl], and [cache_target] configure caching, and [test_query_count] sizes the comparison framework. The retrieval threshold is the lever that decides whether a query type always searches or relies on the model's training knowledge.

When to use it

• You are building a research or assistant feature where stale or hallucinated facts are unacceptable.
• You need responses grounded in current web results with inline source citations.
• You want to tune the retrieval threshold per use case so creative tasks don't over-ground.
• You need to parse grounding metadata and render clickable source links.
• You want a quality pipeline that flags low-grounding responses for review.
• You want to compare grounded vs ungrounded responses to decide where grounding earns its cost.

Example output

Expect a Gemini configuration with google_search_retrieval and a [retrieval_threshold], plus grounding-aware prompt templates referencing [topic_area] and [time_window] and instructing the model to cite sources. The parsing step turns groundingMetadata (chunks, supports, search queries) into a citation UI with inline links. Later steps add a quality evaluation that stores metrics in [analytics_store] and flags responses below [quality_threshold], a caching layer with [news_ttl] and [reference_ttl], and a comparison test over [test_query_count] queries that scores accuracy, recency, and cost. It blends config, parsing code, and evaluation, with the citation rendering treated as a first-class feature so users can verify every grounded claim.

Pro tips

• Set [retrieval_threshold] per use case: low for factual queries that should always search, higher for creative or opinion tasks.
• Write prompts that name the [topic_area] and [time_window] and explicitly ask the model to cite sources, which triggers attribution.
• Render the groundingSupports mapping as inline citations so users can trace each claim to its source chunk.
• Cache stable queries with [reference_ttl] and volatile ones with [news_ttl]; caching news too long defeats the point of grounding.
• Evaluate citation quality on real queries before trusting it; flag anything below [quality_threshold] for human review.
• Use the [test_query_count] comparison to find query types that don't benefit from grounding and run those ungrounded to save cost and latency.