Research Anything:
How Gemini (and Similar LLMs) “Research”
The following Knowledge Card has been updated to reflect the significant advancements in Large Language Models (LLMs) such as Google’s Gemini — notably the introduction of Deep Research and enhanced Reasoning and Multimodality capabilities since March 2024.
Modern LLMs like Gemini 2.5 now perform research through multi-step planning, self-correction, and autonomous information gathering that go far beyond traditional retrieval and synthesis.
Key Research Capabilities in Modern LLMs (e.g., Gemini 2.5)
| Capability | March 2024 (Basic) | October 2025 (Advanced) |
|---|---|---|
| Data Access | Accessing and processing information from a massive, static training dataset. | Dynamic Web & Tool Use: Autonomous searching and deep browsing of the live web to find relevant, up-to-date information, integrated with Google Drive and proprietary research platforms. |
| Reasoning | Understanding context and nuances of language (statistical association). | Thinking Models & Multi-Step Planning: Breaks complex problems into smaller sub-tasks, self-corrects, and reasons iteratively before final output. |
| Synthesis | Gathering information from multiple sources and summarizing it. | Deep Research & Comprehensive Reports: Executes multi-step analysis, evaluates inconsistencies, and synthesizes structured reports, often with Audio Overviews. |
| Source Type | Primarily text and code from the training data. | Natively Multimodal: Processes text, images, audio, video, and PDFs simultaneously within a single context window (up to 1 million tokens or more). |
| Fact-Checking | Fact-checking is recommended for the user. | Internal Verification Mechanisms: Verifier models (e.g., Gemini 2.5 Pro) internally scrutinize outputs to reduce hallucinations. |
| Iterative Refinement | Can refine results based on new information uncovered. | Agentic Behavior: Plans, executes, and adapts research tasks autonomously — an emerging “AI Co-Scientist” model with Generation, Reflection, and Ranking agents. |
Important Considerations
- Still Not Sentient: LLMs process patterns and associations to produce logical outputs but lack consciousness or true understanding.
- The Hallucination Challenge: Although improved grounding reduces errors, fabrication risks remain.
- Fact-Checking is Critical: Always verify information and sources provided by the model — especially in specialized domains.
- Risk of Bias: Outputs may reflect biases in training data; careful prompting helps mitigate them.
- Efficiency vs Comprehensiveness: Users can set “thinking budgets” to balance speed and depth, choosing between quick answers and full Deep Research reports.
Written by Google Research Team (October 2025); Formatted for LibGuides by ChatGPT (GPT-5); Edited by Peter Z. McKay.