Back to guide/General Productivity

Research with Google Search Grounding

Gemini's Google Search grounding provides real-time information with citations. The bias-checking and verification queries encourage critical evaluation of AI-retrieved information.

gemini-promptstopictimeframe
Edit View
Prompt
Research {{topic}} using current information.

I need:
1. **Current facts**: What is the latest information as of today? Include specific numbers, dates, and sources.
2. **Recent developments**: What changed in the last {{timeframe}}? List chronologically.
3. **Key players**: Who are the main companies/people involved and what's their current position?
4. **Consensus vs debate**: What do most sources agree on? Where is there disagreement?
5. **Primary sources**: For each major claim, identify the original source (not a summary article)

Quality requirements:
- Distinguish between confirmed facts and reported/rumored information
- Note if a source has a potential bias (e.g., company blog vs independent research)
- If information is rapidly changing, note the date of the most recent data point
- Do NOT include information you're unsure about — gaps are better than errors

End with 3 specific search queries I can use to verify your most important claims.

Variables to customize

{{topic}}{{timeframe}}

Why this prompt works

Gemini's Google Search grounding provides real-time information with citations. The bias-checking and verification queries encourage critical evaluation of AI-retrieved information.

Save this prompt to your library

Organize, version, and access your best prompts across ChatGPT, Claude, and Cursor.

Related prompts

Task Decomposition Agent

Forcing the agent to plan before acting prevents premature execution and wasted steps. Explicit dependency mapping enables parallel execution and catches logical gaps early.

Tool Selection Agent

The ReAct pattern (Reason + Act) creates an explicit reasoning trace that improves tool selection accuracy. The error-handling rule prevents infinite retry loops.

Prompt Compressor

Explicitly requiring all functional requirements to be preserved prevents the model from over-compressing and losing critical instructions.

Memory Management Agent

Explicit memory read/write instructions create agents that improve over time. Categorization keeps memories organized, and the deduplication rule prevents context bloat.