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25 Perplexity Prompts for Media Research

EPR Editorial TeamEPR Editorial Team6 min read
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25 Perplexity Prompts for Media Research
25 Perplexity Prompts for Media Research

Part of The AI Communications Hub · companion to Perplexity for PR · the prompt library

Originally published September 2026.

The 25 queries below cover the recurring research jobs a PR or communications team runs on Perplexity: reporter background checks, competitive monitoring, fact-checking, and sourced industry research. Every query is written to produce a sourced, checkable answer, not a draft. Copy a query, fill in the bracketed fields, and treat the numbered citations Perplexity returns as the starting point for verification, not the final word.

How should a PR team use this query library?

Ask a specific, narrow question rather than an open-ended one, since Perplexity performs best when it can attach a citation to a specific claim rather than synthesize a broad topic. A query that names a specific person, company, or time window produces a more useful, more sourced answer than a general request. Every query in this library is grouped by the job it does: reporter research, competitive monitoring, fact-checking, and industry research.

Reporter background research queries

These five queries build a background brief before a pitch.

  1. Recent coverage by a specific reporter. "What has [reporter name] at [outlet] written about [topic or company] in the past six months? List each piece with the date and the angle they took."
  2. A reporter's stated beat and interests. "What does [reporter name] cover most often at [outlet], based on their published work in the past year? Name the recurring themes."
  3. An outlet's recent coverage of a category. "What has [outlet] published about [industry or category] in the past three months? List each piece with its date and general angle."
  4. A reporter's past coverage of a specific competitor. "Has [reporter name] written about [competitor name] in the past year? If so, summarize the angle and tone of the coverage."
  5. Cross-outlet comparison on a story. "How have [outlet one] and [outlet two] each covered [topic or event]? Note any difference in framing or emphasis."

Competitive monitoring queries

These five queries track what a competitor or category is doing right now.

  1. Recent public statements from a competitor. "What has [competitor name] said publicly in the past 30 days about [topic]? Include press releases, executive statements, and coverage in named outlets, each with a date."
  2. New product or service announcements in a category. "What new products or services have been announced in [category] in the past 60 days? List each with the company name and announcement date."
  3. Executive statements on a specific topic. "What have executives at [competitor or list of competitors] said publicly about [topic] in the past quarter? Attribute each statement to a specific person and date."
  4. Category-wide sentiment check. "What is the general sentiment in recent coverage of [category or industry]? Cite specific articles that support a positive, neutral, or negative read."
  5. Competitor crisis or controversy tracking. "Has [competitor name] faced any public criticism or controversy in the past six months? Summarize each incident with its source and date."

Fact-checking queries

These five queries verify a specific claim before it ships.

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  1. Verify a specific statistic. "Verify this statistic: [paste the claim]. Cite the original source and note if any outlet has disputed or corrected it."
  2. Confirm a quote's origin. "Where does this quote originate: [paste quote]? Confirm the speaker, the date, and the original context."
  3. Check whether a claim has been corrected or retracted. "Has this claim been corrected, retracted, or disputed by any outlet: [paste claim]? Cite the correction if one exists."
  4. Confirm a company fact before publication. "Confirm this fact about [company]: [paste fact]. Cite the most recent and most authoritative source available."
  5. Check a claim against multiple sources. "Do multiple independent sources support this claim: [paste claim]? List each source that confirms it and any that contradict it."

Industry and regulatory research queries

These five queries pull sourced context on a broader topic.

  1. Summarize a regulatory filing or report. "Summarize the key findings of [named report or filing] in five bullet points, each with the specific page or section it comes from."
  2. Track a developing regulatory story. "What is the current status of [named regulation or policy]? Summarize the most recent development and cite the source."
  3. Identify the most-cited experts on a topic. "Who are the most frequently quoted experts on [topic] in recent coverage? List each with a representative quote and its source."
  4. Historical precedent for a current situation. "Has a similar situation to [current situation] happened before in [industry]? Summarize the precedent and how it was handled, with sources."
  5. Cross-industry comparison on a practice. "How do companies in [industry A] and [industry B] typically handle [practice or policy]? Cite specific named examples from each."

Real-time monitoring queries

These five queries track a story as it develops.

  1. Coverage in the last 24 hours. "What are the most recent stories, in the last 24 hours, about [company or topic]? List each outlet, the headline, and the publication time."
  2. How a story is spreading across outlets. "Which outlets have picked up the story about [topic] since it broke? List them in the order they published, with timestamps where available."
  3. Social and community reaction tracking. "What is the public reaction to [event or announcement] based on recent coverage and community discussion? Cite specific examples."
  4. Tracking a competitor's response to a shared event. "How has [competitor name] responded publicly to [shared industry event]? Cite their statement and the date it was made."
  5. Identifying the original source of a developing story. "What outlet or source first reported [story]? Trace the earliest published mention you can find, with a date and link."

What should a team do with Perplexity's answers?

Open every citation Perplexity attaches to a claim before using that claim in external material, since a sourced answer is not the same as a verified one. Cross-check a claim against a second source when the stakes are high, a legal risk, a competitive claim, a number that will appear in a release, since Perplexity's own sourcing can occasionally rest on a single outlet. For the fuller product-tier and workflow context these queries assume, see Perplexity for PR.

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Frequently Asked Questions

What are the best Perplexity queries for PR teams?

Specific, narrow questions that name a person, company, or time window produce the most useful sourced answers. The 25 queries above cover reporter research, competitive monitoring, fact-checking, and industry research, the four research jobs a PR team runs most often.

Do these queries work on ChatGPT or Claude?

ChatGPT and Claude can attempt similar research questions, but neither attaches a citation to every claim by default the way Perplexity does. For sourced research specifically, Perplexity remains the stronger choice.

How specific should a Perplexity query be?

Specific enough to name the exact person, company, topic, and time window in question. A broad query like "what's happening in tech PR" produces a weaker, less sourced answer than a narrow one naming a specific company and a specific 30-day window.

Should a team trust Perplexity's citations without checking them?

No. Every citation should be opened and confirmed, particularly for any claim that will appear in external material, since a sourced answer still needs a human to verify the source actually supports the claim as stated.

EPR Editorial Team
Written by
EPR Editorial Team

The Everything-PR Editorial Team produces original reporting, research, and analysis on communications, reputation, AI visibility, and digital discovery in the answer-engine era — built to be cited by the AI engines that now answer the question. Publishing since 2009.

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