Training course · Perplexity · Engineering

Perplexity for Engineers

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 Engineers” is a hands-on course for roles such as Design and development engineers, Project engineers in plant and machine building and Test and validation engineers. Rather than learning Perplexity in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Engineering.

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Quality is a key focus: you learn to assess sources, cross-check claims and recognise typical failure patterns such as outdated or misattributed information.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

Engineers in design, development, plant engineering and technical planning spend much of their time on research, documentation, coordination and calculations. Generative AI can reduce that share considerably: AI assistants summarise standards and technical literature, structure requirement and design specifications, support requirements analysis, generate calculation scripts in Python or spreadsheets and produce technical documentation and reports. Automation helps with change management, comparing document revisions and analysing test and measurement data. Creative problem-solving, plausibility checks and responsibility for technical safety remain with the experts. Using AI for routine work and as a sparring partner frees time for real engineering: developing concepts, evaluating variants and delivering robust solutions.

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?

  • Design and development engineers
  • Project engineers in plant and machine building
  • Test and validation engineers
  • Technical writers
  • Engineers in planning and engineering consultancies

Typical challenges we address

  • Evaluating extensive standards, guidelines and requirements efficiently
  • Producing technical documentation and reports alongside development work
  • Analysing and interpreting test and measurement data quickly
  • Managing changes traceably across many documents and stakeholders

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 requirements analysis, standards research and technical documentation.
  • Have AI create calculation and analysis scripts and validate them systematically.
  • Protect engineering know-how and observe product safety and liability requirements 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: Engineering
  • Structuring requirements
  • Standards and literature review
  • Calculation scripts
  • Test data analysis
  • Technical documentation
  • Change management support
Module 7 Data protection, quality & transfer to daily work
  • Design data, drawings and development results are trade secrets and may be subject to export control; they belong only in approved AI environments. Product safety and liability law apply unchanged to safety-related designs, so AI output must be verified and documented traceably.
  • 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

Structuring requirements

An AI assistant analyses customer requirements and specifications, spots contradictions, gaps and vague wording and turns them into a structured, verifiable requirements list for the design specification.

Standards and literature review

AI summarises relevant sections of standards, guidelines and technical papers, creates comparison tables and checklists and references the passages, which engineers check in the original.

Calculation scripts

Instead of coding formulas by hand, describe the calculation and receive a commented Python script or spreadsheet, for example for sizing, tolerance stack-ups or unit conversions, ready for testing and validation.

Test data analysis

Test bench data is cleaned, statistically analysed and visualised using AI-generated analysis code, anomalies are flagged and a draft test report is created.

Technical documentation

Design data, notes and previous documents are turned into drafts of operating manuals, maintenance handbooks, specifications and project reports with consistent terminology.

Change management support

AI compares document revisions, summarises technical changes clearly, identifies affected documents and interfaces and prepares change requests for the change board.

Prerequisites

Experience with online research in a professional context; no prior AI knowledge is required. You have an engineering or technical background and work in development, design, planning or testing.

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

Design data, drawings and development results are trade secrets and may be subject to export control; they belong only in approved AI environments. Product safety and liability law apply unchanged to safety-related designs, so AI output must be verified and documented traceably.

Suggested dates

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

  • Tue, 24 November 2026 Online Request
  • Thu, 3 December 2026 Kaiserslautern Request
  • Fri, 11 December 2026 Online Request
  • Thu, 7 January 2027 Augsburg Request
  • Tue, 19 January 2027 Online Request
  • Mon, 1 February 2027 Salzburg 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 calculations for safety-related designs?

Only as support with full verification. Language models can get formulas, units or standard values wrong. Have calculation steps output as traceable code or formulas, check them against standards, hand calculations or validated software and document the check. Responsibility remains with you.

How do we protect design data?

Use only approved enterprise environments where inputs are not used for training. Also observe export control rules, for example for dual-use technologies, and agree with IT and export control which information may go into which tools. We discuss practical rules in the course.

How much does “Perplexity for Engineers” 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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