Training course · Perplexity · Research & Science

Perplexity for Researchers

AI-powered research with Perplexity: cited answers, source control, Spaces, Deep Research and systematic fact-checking.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Perplexity for Researchers” is a hands-on course for roles such as Doctoral candidates, Research associates and postdocs and Professors and research group leaders. Rather than learning Perplexity in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Research & Science.

Perplexity combines language models with live web search and delivers answers backed by numbered citations. That makes it particularly suited to work where currency and traceability matter: market and competitor monitoring, research into regulation and standards, supplier and partner due diligence, or preparing decision papers. In this practical day you learn to phrase research questions precisely, steer the choice of sources deliberately and use follow-up questions to turn an initial answer into a robust research result.

You work with Spaces, where you group research on a topic, add your own files and set instructions for the team. With Deep Research you produce extensive reports that evaluate many sources and bring them together in a structured way. Quality is a key focus: you learn to assess sources, cross-check claims and recognise typical failure patterns such as outdated or misattributed information. We also address data protection and licensing questions for business use. You leave with a reusable research process tailored to your own function.

Academic work is shaped by literature reviews, data analysis, grant writing and publication pressure. Generative AI can help at every stage: navigating and summarising the literature, sharpening research questions, writing analysis code, polishing manuscripts and preparing funding applications. AI agents can automate recurring steps such as data cleaning or cross-checking references. At the same time, AI touches core questions of research integrity: who is an author, how is AI use disclosed, how do you prevent fabricated references? Researchers therefore need more than prompting tricks – they need a methodologically sound, transparent and reproducible way of working with AI tools.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Doctoral candidates
  • Research associates and postdocs
  • Professors and research group leaders
  • Research offices and grant managers
  • Corporate R&D teams

Typical challenges we address

  • An ever-growing body of literature that is hard to keep up with
  • Heavy writing load for proposals, reports and papers
  • Data analysis that requires programming skills
  • Uncertainty about AI rules set by journals, funders and universities

What you will be able to do

  • You phrase precise research questions and steer the choice of sources deliberately.
  • You assess sources by origin, currency and reliability and trace claims back to the original.
  • You organise research in Spaces and combine web sources with your own documents.
  • You use Deep Research to produce structured reports and decision support.
  • Use AI for literature work and writing without breaching research integrity standards.
  • Produce traceable analysis code with AI support and document it reproducibly.
  • Disclose AI use transparently in line with journal, funder and institutional requirements.

Course outline

Module 1 Foundations of AI-powered research
  • How it works: language model, live web search and citations
  • How it differs from a classic search engine and a general chat assistant
  • Framing research questions precisely: time frame, region, source type
  • Follow-ups and drilling down instead of one-off queries
Module 2 Steering and assessing sources
  • Narrowing sources: web, academic literature, your own files
  • Targeting primary sources, official websites and specialist publications
  • Assessing source quality: origin, currency, vested interests
  • Tracing quotes and figures back to the original source
Module 3 Spaces and teamwork
  • Setting up Spaces for topics, projects or client requests
  • Uploading your own documents and combining them with web sources
  • Storing instructions for consistent output formats
  • Sharing research and turning it into pages or reports
Module 4 Deep Research and structured reports
  • Tackling broad questions with Deep Research
  • Producing market overviews, competitor comparisons and topic briefs
  • Turning results into tables, side-by-side comparisons and executive summaries
  • Recognising the limits of in-depth research and refining results
Module 5 Fact-checking, data protection and workflow
  • Systematic fact-checking: counter-research and spotting contradictions
  • Typical failure patterns: outdated, misattributed or fabricated details
  • Data protection, enterprise licences and handling confidential queries
  • A reusable research process and source documentation
Module 6 Practice lab: Research & Science
  • Navigating the literature
  • Sharpening research design
  • Writing analysis code
  • Revising manuscripts
  • Preparing grant proposals
  • Analysing qualitative data
Module 7 Data protection, quality & transfer to daily work
  • Codes of research integrity apply: AI cannot be an author, its use must be disclosed and all references must be checked against the original. Personal research data, for example from studies involving participants, is subject to the GDPR and ethics approvals; unpublished results and peer reviews must be treated as confidential.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Navigating the literature

AI summarises abstracts and full texts, extracts methods, samples and findings into comparison tables and helps formulate search strings for scholarly databases – every source is then verified in the original.

Sharpening research design

Research questions, hypotheses and operationalisations are stress-tested with AI as a critical sparring partner to spot gaps, confounders and alternative explanations early. Control conditions and limits to generalisability are discussed as well.

Writing analysis code

Researchers without deep programming skills use AI to produce Python or R scripts for data cleaning, statistics and visualisation, documenting them clearly to ensure reproducibility.

Revising manuscripts

AI supports language editing, structuring and shortening of academic texts, checks terminology for consistency and helps draft responses to reviewers without taking over authorship. Your argument stays your own.

Preparing grant proposals

Funding guidelines are turned into checklists, work packages and milestones are structured, and sections such as impact plans or lay summaries are drafted in several versions.

Analysing qualitative data

Interview transcripts and open-ended responses are pre-coded and grouped thematically with AI; researchers review, refine and document the coding frame according to established methodology. Inter-coder checks remain essential.

Prerequisites

Experience with online research in a professional context; no prior AI knowledge is required. Experience of academic work in a discipline; programming skills are helpful but not required.

Technical requirements

A laptop with an up-to-date browser and a Perplexity account; we recommend Pro or Enterprise access to use Spaces with files and Deep Research in full.

Data protection & compliance

Codes of research integrity apply: AI cannot be an author, its use must be disclosed and all references must be checked against the original. Personal research data, for example from studies involving participants, is subject to the GDPR and ethics approvals; unpublished results and peer reviews must be treated as confidential.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 Online Request
  • Mon, 30 November 2026 Augsburg Request
  • Thu, 17 December 2026 Online Request
  • Fri, 8 January 2027 Online Request
  • Fri, 15 January 2027 Kaiserslautern Request
  • Mon, 25 January 2027 Online Request

Frequently asked questions

How does Perplexity differ from ChatGPT or a search engine?

A search engine returns a list of links, and a general chat assistant often answers from its training data. Perplexity searches the web at the moment you ask, summarises what it finds and backs each statement with citations. That makes it particularly well suited to current, verifiable research – although you still need to assess the sources yourself.

Can I rely on Perplexity's citations?

Citations make verification much easier, but they do not replace it. Occasionally a statement is attributed to a source that does not say quite that, or an outdated page is used. In the course you learn a lean fact-checking routine that lets you quickly verify critical statements against the original source.

Is Perplexity suitable for confidential research within a company?

Enterprise plans offer additional commitments on data use and administration. Even so, you should not put trade secrets or personal data into queries unless your internal policy explicitly allows it. We discuss how to phrase queries so that sensitive details stay protected while the research remains useful.

Can I use AI to write my PhD thesis?

That depends on your doctoral regulations and institutional policies. AI is often permitted for language editing and brainstorming if its use is disclosed and your own intellectual contribution remains clear. In the course you learn to document use properly and assess common grey areas with confidence.

How do I avoid fabricated references?

Language models can produce plausible-sounding citations that do not exist. Work with tools that query real databases, ask for DOIs and verify every source in the original. The course demonstrates a workflow that combines AI-assisted searching with reliable reference management.

How much does “Perplexity for Researchers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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