Deep Research with LLMs: How ChatGPT, Gemini & Perplexity Super-Charge Insight Gathering

In this Talking AI episode, co-founders Ray and Will Poynter break down the rise of Deep Research—an AI workflow that combines live web search, long-context reasoning and citation-linked summarisation.

What You'll Learn

• What “Deep Research” Means
  • Why multiple vendors (ChatGPT, Gemini, Perplexity) use the same term
  • How it differs from standard chat prompts or Canvas sessions
• Speed vs. Depth Trade-offs
  • 178 web hits & 30 sources in < 10 min: when the wait is worth it
  • When iterative Canvas-style prompting is still faster
• Source Quality & Hallucination Control
  • Forcing reputable domains, spotting blocked sites (e.g. BBC, pay-walled press)
  • Using the final summary first, then drilling into citations
• Practical Use-Cases
  • Entering new verticals (e.g., canned-coffee market in Japan, TfL transport data)
  • Generating monthly polling digests, executive briefings, podcast scripts
• Limitations & Work-arounds
  • Verbosity, missing premium sources, daily search caps on free tiers
  • Future outlook: personalised memory, task-based scheduled research, dashboard feeds

Key Takeaways

1. Deep Research = AI research assistant on steroids—ideal for zero-to-sixty topic ramp-ups.
2. Quality in, quality out—specify sources and always sanity-check citations.
3. Iterate smartly—use summaries to steer; don’t wade through 20 pages blind.
4. Free to start—Perplexity’s free tier offers one robust run per day; ChatGPT-4o currently leads on depth and reasoning but costs more.