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