Training course · Perplexity · IT Service Desk & Support

Perplexity for IT Service Desk and Support Teams

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

This course connects Perplexity with the everyday processes of IT Service Desk & Support. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

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Quality is a key focus: you learn to assess sources, cross-check claims and recognise typical failure patterns such as outdated or misattributed information.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

The IT service desk is the first point of contact for incidents, requests and questions about devices, software and access. Rising ticket volumes, more complex IT estates and hybrid working put support teams under growing pressure. Generative AI and AI agents are changing this field particularly strongly: they classify and prioritise tickets, suggest fixes from the knowledge base, write clear replies for users, turn resolved cases into knowledge articles and answer standard requests through self-service around the clock. Scripts for recurring admin tasks can also be produced faster with AI. That leaves specialists more time for complex incidents, problem management and service improvement.

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?

  • First- and second-line service desk analysts
  • Team leads and service desk managers
  • IT administrators and end-user support
  • IT service management and knowledge management leads
  • Internal and external support providers

Typical challenges we address

  • High ticket volumes with many repetitive requests
  • Incomplete or outdated knowledge bases
  • Users with very different levels of IT literacy
  • Meeting service levels with limited resources

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.
  • Handle tickets faster, more consistently and more user-friendly with AI support.
  • Build an up-to-date, well-structured knowledge base with AI.
  • Design self-service and automation solutions with a sensible handover to people.

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: IT Service Desk & Support
  • Classifying and prioritising tickets
  • Fix suggestions from knowledge
  • Clear user communication
  • Writing knowledge articles
  • Self-service and chatbots
  • Scripts for routine tasks
Module 7 Data protection, quality & transfer to daily work
  • Tickets often contain personal data, access details and information about IT infrastructure that must not flow into unapproved AI services; passwords and keys never belong in prompts. Using ticket data to evaluate staff performance requires compliance with the GDPR and employee representation rules, and AI-generated scripts must be reviewed according to IT security policies.
  • 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

Classifying and prioritising tickets

Incoming tickets are categorised by type, urgency and affected service, missing information is detected and follow-up questions to users are prepared automatically. Duplicate tickets and related incidents are detected too.

Fix suggestions from knowledge

Drawing on the knowledge base, past tickets and vendor documentation, AI suggests resolution steps, which the analyst checks and adapts to the specific situation. Sources are always cited.

Clear user communication

Technical fixes are turned into clear, friendly step-by-step guides, incident status updates are drafted and replies are provided in several languages. Fewer follow-up questions arise, and users can solve simple issues themselves.

Writing knowledge articles

Resolved tickets and notes become structured knowledge articles with symptom, cause and resolution; outdated articles are identified and flagged for review. Team knowledge keeps growing and stays current.

Self-service and chatbots

AI agents answer standard questions such as password resets, VPN setup or printer issues in the self-service portal and hand complex cases to the team with a summary.

Scripts for routine tasks

Administrators use AI to create PowerShell or Bash scripts for recurring tasks such as user provisioning or reports, and test them carefully before use. A review before production use is mandatory.

Prerequisites

Experience with online research in a professional context; no prior AI knowledge is required. Experience in IT support or IT service management; basic scripting 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

Tickets often contain personal data, access details and information about IT infrastructure that must not flow into unapproved AI services; passwords and keys never belong in prompts. Using ticket data to evaluate staff performance requires compliance with the GDPR and employee representation rules, and AI-generated scripts must be reviewed according to IT security policies.

Suggested dates

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

  • Wed, 25 November 2026 Online Request
  • Tue, 8 December 2026 Salzburg Request
  • Wed, 16 December 2026 Online Request
  • Wed, 20 January 2027 Bremen Request
  • Tue, 2 February 2027 Online Request
  • Wed, 17 February 2027 Augsburg 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.

Will an AI chatbot replace first-line support?

An AI agent can resolve many standard requests on its own and pre-qualify tickets. Complex incidents, exceptions and users who need personal help stay with the team. Success depends on a clear handover with a summary. In the course you design such handovers and define which cases are suitable for automation.

How do I prevent incorrect fix suggestions from AI?

Ground the AI in verified knowledge such as your knowledge base and vendor documentation, ask for sources and check critical steps before executing them. For changes to systems, the four-eyes principle applies. The course shows how to build quality assurance into the support process.

How much does “Perplexity for IT Service Desk and Support Teams” 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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