How to Use AI Tools for Research: A Practical Framework for 2025
AI tools can dramatically accelerate research work — but most people use them wrong. Here is a systematic approach to research acceleration that actually works in 2025.
How to Use AI Tools for Research: A Practical Framework for 2025
The Research Stack
Different AI tools excel at different research phases:
- Perplexity Pro: Current information, cited sources, initial landscape mapping
- Claude Pro: Synthesizing information, analyzing documents, drawing implications
- Consensus (free): Academic paper search with AI summaries
- Elicit (freemium): Systematic literature review, extracting findings from papers
Phase 1: Landscape Mapping (Perplexity)
Start any research project with Perplexity: “What are the main perspectives/debates/findings on [topic]? Give me a structured overview with the key camps and their core arguments.”
This 5-minute step would previously require reading dozens of sources. The output gives you a map of the territory — what questions matter, what camps exist, what evidence to look for.
Phase 2: Deep Dive on Specific Questions (Claude)
Once you know what questions matter, use Claude to explore them deeply. Upload relevant documents (Claude’s 200K context window handles long PDFs). Ask Claude to: summarize key arguments, identify assumptions, surface counterarguments, and explain implications for your specific context.
Phase 3: Synthesis and Writing (Claude)
After gathering information, Claude helps transform raw notes into structured analysis. The prompt: “Here are my research notes on [topic]. Synthesize these into a coherent argument for [audience] in [format]. Key point I want to make: [your thesis].”
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