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AI for research

Using AI to accelerate research without trading reliability for speed. Source-grounded search, literature reviews, citation hygiene, and the verification habits that separate research from hallucination.

Key takeaways

  • Use Perplexity or Elicit for source-grounded answers. Plain ChatGPT will hallucinate citations under deadline pressure.
  • Always verify every quoted statistic by opening the cited source. AI-cited DOIs are still wrong a meaningful fraction of the time.
  • AI accelerates SYNTHESIS, not discovery. Search humans first, then ask AI to summarise what you found.
  • Keep a research log that pairs every claim with its source URL. Future-you needs this when reviewers question a number.

Frequently asked questions about this category

Can I trust ChatGPT to do research for me?

Only with tool use enabled (web search, code interpreter) and only with manual verification of every citation. Plain ChatGPT in chat mode confidently fabricates sources, especially under specific date or DOI requirements.

What is the difference between Perplexity, Elicit, and Consensus?

Perplexity is a generalist research assistant with web citations. Elicit specialises in academic literature reviews and is the strongest at extracting structured findings from papers. Consensus aggregates evidence across studies for a given claim. Use them together — each catches gaps the others miss.

How do I cite AI-generated content properly?

For factual claims, cite the underlying primary source the AI surfaced, not the AI itself. For original AI output (a generated summary, a translation), disclose the model and date per your style guide (APA, Chicago, and MLA now all have AI-citation guidance).

How do I keep AI research notes organised?

One Notion or Obsidian page per topic, with every claim followed by its source URL and a one-line "why this matters" gloss. Future-you reviewing 50 sources six weeks later cannot reconstruct intent without this discipline.

Is AI making academic research faster or worse?

Both, depending on the user. For researchers who treat AI as a synthesis accelerator on top of human-led discovery, faster and equally rigorous. For those who treat AI as a substitute for reading, lower-quality work with citation problems that reviewers are increasingly trained to spot.