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Search Guide for Perplexity AI

2025 Deep Research: Production of professional studies at the minute level

What is perplexity?

Perplexity is an AI driven "search engine that answers questions." and traditional search engines (e.g., Google returns a long list of links, Perplexity Provides you with structured answers by reading the content of the web page and consolidating information, and clearly indicates the source of each information. 2025 It's introduced in the year. Deep Research (Depth Study) The function is to put its productivity to a peak, making it a preferred tool for professional analysts, scientists and policymakers.

Core strengths: Deep Research Dozens of searches, hundreds of relevant sources of information can be automatically carried out and a professional study containing summaries, detailed analyses and conclusions can be automatically generated within minutes.
Four search modes

Quick Search Default

Quick answers to simple facts, definitions or routine queries. Responds at extreme speed and suitable for rapid access to debrisified information.

Deep Research Core

Deep excavations on complex themes. Auto-build research paths, integrate hundreds of sources and produce professional reports.

Code Mode

Provides examples of high-quality codes and debugging suggestions for programming optimization, retrieval of technical documents, Stack Overflow and GitHub.

Academic mode

The search of peer-reviewed academic papers, periodicals and professional databases is suitable for the preparation of literature reviews and scientific research.

Deep Research Work Flow Resolution

Multi-dimensional problem resolution

The system will use LLM first when it receives your research theme Dismantling the problem and identifying multiple sub-dimensional and keywords to be explored.

Large-scale parallel search

Based on Sonar Models, conducting dozens of related searches on a web-wide basis, accurate location of authoritative agency reports, news messages and real-time data.

In-depth reading and aggregation

AI simulates human reading habits, extracts core data, views and evidence from every page, and discards redundant and irrelevant information.

Structured report generation

Using RAG (Retrieval Enhancement Generation) architecture, complete reporting is done according to the "background-state-analysis-forward-conclusion " logical framework output.

⚔️ AI Search capability is much more than a puzzle
Functional characteristics Perplexity ChatGPT (Search) Traditional Google
Real-time sex Zipper, very high (minute update) High High
Transparency of source ⭐ Quote source • Partially labeled ♪ We'll have to look for it manually ♪
In-depth study Deep Research • Multiple conversations :: Manual writing required
Fact-based accuracy rate ⭐ 93.9% (SimpleQA) Good. ❌ Depends on the searcher
93.9% Fact-based accuracy rate
3 min Report generation speed
500 million+ Number of pages processed daily
Sonar Core driver model

Deep field: How to use Perplexity Deep Research to solve specific challenges in 2025?

In the era of information overload, search is no longer a difficult task. Filtering and Integration of Information The real challenge is that. Perplexity Deep Research The presence of the former was reduced to a few minutes of manual research that would have taken several days. The following are three typical scenarios of operational applications:

1. Business competition analysis: One key to generating the backsliding of competition

The scene description: A founder of a start-up company needs to know that competitors have been in the past 12 years. Months of market strategy, changes in investment financing and negative feedback from users. Using traditional searches, he needed to look at dozens of pages of news, financial and social media posts. And... In Perplexity, he simply enters: “The comprehensive analysis company X market movements in 2024, key partners and core user slots on social media. ” AI will automatically retrieve its financial summary, the science blog commentary article and X/Reddit The discussion at the above level was eventually compiled into a comparative and clear table and analysis.

2. Technical selection research: supporting structural decision-making

The scene description: The technical officer needs to choose between three different open-source RAG frameworks. Perplexity: "Lang Chain, LlamaIndex and RAGFlow Performance, community activity and deployment costs in processing large-scale multi-module data. ” AI will go deep into GitHub The warehouse statistics, the survey papers of the developers ' communities and the official technical white paper directly give recommendations for decision-making that include comparisons of advantages and disadvantages.

3. Real-time financial research: tracking macro dynamics

The scene description: Investors need to know the response of the global semiconductor supply chain following the last US Federal Reserve interest conference. Perplexity handles more than 5 per day Billion pages. That makes sure. The most timely economic analysts ' briefs and official statements by the giant chips are available to help investors make data-driven decisions in the midst of fluctuations.

4. Subscription proposals

  • Free users: Supporting the Deep Research mission five times a day is sufficient to handle routine and occasional in-depth queries.
  • Pro user ($20/month): 500 Deep Research and Free Choices for GPT-4, Claude 3.5 Sonet The bottom model is suitable for high-intensity research needs.

The KiloCell believe that Perplexity is more than a tool, and it represents a completely new paradigm for the future of “knowledge retrieval is conclusion”. Got it. AI Search will be one of the most expensive skills for the world of work in 2025.