Session simulator · Generative AI for Business Insights

Run the workshop,
not just read it.

A live console for practicing prompt structure, walking every business function's AI use case, and testing what you actually retained — everything from the session, simulated in the browser.

prompt-console — simulated session

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01 · Hands-on

Prompt Console

Fill in the R-C-T-F fields and watch the simulated response sharpen in real time. Nothing here calls a live model — it's a rule-based simulation of how structure changes output quality.

Build the prompt

Simulated response

Prompt qualityWeak
Start typing in any field to see the simulated response change.

Coaching tips

  • → Add a Role — tell the AI who to act as (e.g. "senior financial analyst").
  • → Add Context — background, audience, or constraints the AI should work within.
  • → Add a Task — the exact, specific ask, not a vague topic.
  • → Add a Format — tell it how to shape the output (bullets, table, email, summary).

02 · Field guide

Business Application Explorer

Six functions, one console. Pick a function and application to see the workflow, then inspect or edit the exact prompt before optional live generation.

The selected default. Obtain a free-tier key from NVIDIA Build.Get a NVIDIA NIM key ↗Watch provider demo ↗Official channel ↗

Session storage is not a secure vault: same-origin scripts or browser extensions may access it. Keys are sent directly from this browser to the selected provider and clear when this tab closes or you reset the session.

15 applications · MKT

MKT · Application

Ad Copy Generation

  1. STEP 1Enter the offer, audience, channel, and campaign objective.
  2. STEP 2Generate differentiated headline and body variants.
  3. STEP 3Review, validate, and adapt the output before using it.

Built-in simulated sample

Urgency-led Variant C is projected to outperform the control on click-through rate.

Prompt builder

Inspect exactly what will be sent

Enter a NVIDIA NIM (Free Tier) API key to enable live generation.

meta/llama-3.1-8b-instruct

Video walkthroughs · MKT

See the tools in action

Official-channel videos are preferred. Where no single canonical demo exists, the tool's official video or resource library is linked instead.

Real-world use cases · MKT

How teams changed the work

Figures are reported in the linked vendor, company, or third-party case studies. Treat them as directional examples, not guaranteed outcomes.

Jasper

Pilot Company · fuel, travel & logistics

Pilot's content team adopted Jasper to accelerate collaborative, on-brand content production across the organization while keeping teams aligned on tone and messaging.

Before

Content requests bottlenecked through a small team; blogs, social posts, and campaign assets took days to draft, review, and align with the brand voice.

With the tool

Teams draft first versions directly with brand-voice settings, reducing draft turnaround from days to hours and freeing the central team for strategy and review.

Read the source case study ↗

Copy.ai

Copy.ai marketing customers

Customers using Copy.ai's Claude-powered content platform reportedly produced roughly four times the content at about a quarter of the previous cost, with some substantially reducing agency spend.

Before

Businesses relied on freelance writers and agencies for routine content, often publishing one blog post a month at high recurring cost.

With the tool

AI drafts routine content in-house; reported customers moved toward daily publishing at a fraction of their previous spend.

Read the source case study ↗

HubSpot AI

Motorola Solutions

Motorola Solutions used HubSpot's Data Hub and AI-assisted Data Studio to unify more than 123,000 customer records and surface a significant cross-sell opportunity.

Before

Customer data was fragmented across systems, obscuring a unified customer view and making real-time cross-sell analysis difficult.

With the tool

An AI-assisted data layer gives marketing timely access to unified customer information and makes previously buried opportunities easier to act on.

Read the source case study ↗

03 · Knowledge check

30-Question Debrief

Enter your details, answer all 30 questions, then download your private result report.

Participant record · not stored

These details remain in this page only and are used to create your downloaded report.

0/30

01What does 'Generative AI' primarily refer to?

02What is a 'token' in the context of Large Language Models?

03What is the 'context window' of an LLM?

04How does an LLM generate a response?

05Which of the following best describes 'prompt engineering'?

06In the R-C-T-F prompt framework, what does 'C' stand for?

07Which technique involves giving the AI 1-2 sample outputs before asking your real question?

08Why should you ask an AI to 'show its reasoning' for complex tasks?

09What is the recommended approach when an AI's first response isn't quite right?

10Why is it better to paste actual source data into a prompt rather than describing it from memory?

11In AI-assisted decision making, what role should Generative AI ideally play?

12What is a key risk to watch for when using AI-generated numbers or facts?

13Which business function commonly uses AI for resume screening and interview question generation?

14How can Generative AI support the Marketing function?

15Which of the following is a common Finance use case for Generative AI?

16How can AI chatbots support Customer Support teams?

17Which Operations task can Generative AI help automate?

18How does Generative AI support personalized learning in Education?

19Which of these is an example of an AI-powered productivity copilot embedded in everyday office tools?

20What is a practical use of AI in business analytics?

21Which of the following is considered AI-assisted content generation for business documents?

22Why is protecting sensitive data important when using public Generative AI tools?

23What does it mean to 'keep a human in the loop' when using AI at work?

24Why should organizations disclose when content or decisions were AI-assisted?

25What does 'bias' in AI outputs typically result from?

26What should employees consider regarding intellectual property (IP) when using AI content-generation tools?

27Which of the following best describes an AI tool used for meeting productivity?

28What is the main difference between traditional AI and Generative AI?

29Which statement reflects a responsible approach to using AI-generated business insights?

30What is a good first step for applying Generative AI in your own workflow?