Training course · Perplexity · Pharma & Life Sciences

Perplexity for Pharma and Life Sciences

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 Pharma and Life Sciences” is a hands-on course for roles such as Regulatory affairs professionals, Quality assurance and Qualified Persons and Clinical operations and medical writers. Rather than learning Perplexity in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Pharma & Life Sciences.

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Quality is a key focus: you learn to assess sources, cross-check claims and recognise typical failure patterns such as outdated or misattributed information.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.

Pharmaceutical, biotech and medtech companies operate in one of the most heavily regulated environments of all. Documentation, validation and traceability shape daily work from research and clinical trials to manufacturing. Generative AI offers significant potential: AI assistants review scientific literature, help draft study protocols, regulatory documents and SOPs, summarise deviation reports, support pharmacovigilance case processing and prepare medical information for healthcare professionals. Automation supports change control, CAPA tracking and regulatory intelligence. At the same time, as soon as AI touches GxP-relevant processes, validation, data integrity according to ALCOA+ and clear accountability are mandatory. Adoption succeeds when companies clearly separate non-regulated support from GxP use and involve quality assurance early.

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?

  • Regulatory affairs professionals
  • Quality assurance and Qualified Persons
  • Clinical operations and medical writers
  • Pharmacovigilance and drug safety teams
  • Medical affairs and R&D scientists

Typical challenges we address

  • Producing extensive regulatory and scientific documents to a high standard
  • Processing deviations, CAPAs and change controls on time
  • Systematically evaluating growing volumes of literature and regulation
  • Using AI tools in a GxP-compliant, validatable way

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 review, medical writing and regulatory intelligence.
  • Distinguish non-regulated AI use from GxP-relevant applications and understand validation requirements.
  • Safeguard data integrity, confidentiality and patient data protection when using AI.

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: Pharma & Life Sciences
  • Literature review and evidence
  • Medical writing support
  • SOPs and training materials
  • Deviations and CAPA
  • Regulatory intelligence
  • Pharmacovigilance support
Module 7 Data protection, quality & transfer to daily work
  • When AI systems are used in GxP-relevant processes, computerised system validation under EU GMP Annex 11 or 21 CFR Part 11 and data integrity according to ALCOA+ must be ensured. Clinical trial and pharmacovigilance data contain health data under Article 9 GDPR and may only be processed in pseudonymised form in approved environments.
  • 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

Literature review and evidence

An AI assistant summarises scientific publications, extracts study design, endpoints and results into tables and supports systematic reviews, with experts checking every data point against the original source.

Medical writing support

AI creates outlines and first drafts of study protocols, clinical study reports, investigator brochures or modules of the regulatory dossier based on approved templates and source data.

SOPs and training materials

Process descriptions and regulations are turned into draft SOPs, training slides and comprehension questions, which are then reviewed and approved through the validated document management system.

Deviations and CAPA

AI summarises deviation reports, structures root cause analyses, finds similar past cases and suggests CAPAs, which quality assurance evaluates and decides on.

Regulatory intelligence

Automations collect new guidelines and publications from the EMA, national authorities or the FDA; AI summarises changes and maps them to affected products and processes for an impact assessment.

Pharmacovigilance support

AI helps pre-structure case reports from unstructured text, extract relevant information on product, event and patient and detect duplicates, while medical assessment remains with qualified staff.

Prerequisites

Experience with online research in a professional context; no prior AI knowledge is required. You work in the pharmaceutical, biotech or medtech industry and know the GxP basics of your area.

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

When AI systems are used in GxP-relevant processes, computerised system validation under EU GMP Annex 11 or 21 CFR Part 11 and data integrity according to ALCOA+ must be ensured. Clinical trial and pharmacovigilance data contain health data under Article 9 GDPR and may only be processed in pseudonymised form in approved environments.

Suggested dates

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

  • Fri, 27 November 2026 Online Request
  • Wed, 9 December 2026 Bremen Request
  • Fri, 15 January 2027 Online Request
  • Mon, 18 January 2027 Salzburg Request
  • Mon, 25 January 2027 Online Request
  • Thu, 4 February 2027 Kaiserslautern 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.

Do AI tools need to be validated in pharma?

It depends on the use. For activities without GxP relevance, such as internal communication or literature overviews, general IT and data protection rules apply. If results feed into GxP-relevant decisions or records, a risk-based validation approach is required. We show you how to classify use cases.

How do we ensure data integrity?

AI output must be attributable, traceable and verifiable. That means documented inputs and prompts, clearly assigned reviewers, versioning and release through your validated systems. In the course we discuss how ALCOA+ principles apply to AI-supported work steps. Clear retention rules complete the picture.

How much does “Perplexity for Pharma and Life Sciences” 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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