The skills behind every Rana-AI Tech course
We don't measure learning in hours watched β we measure it in skills demonstrated. Every Rana-AI Tech course maps to a structured competency framework: named, observable abilities a learner proves by passing each module's knowledge check. The result is a credential that says exactly what your people can do with AI.
Skill domains
The framework is organized into four progressive domains. Each lists the specific competencies a learner masters as they move through the catalog.
Foundational AI Literacy
100 27 competenciesThe plain-English foundations every employee needs: what AI is, how to talk to it, how to choose a tool, and how to use it safely.
- Explain what AI and LLMs are in plain terms
- Hold an effective AI conversation and refine outputs
- Apply core AI safety habits: privacy, verification, human oversight
- Choose an appropriate AI tool for a task
- Produce work products with AI (documents, images, templates)
- Ground AI with your own files, the web, and research
- Describe what an AI agent is and its guardrails
- Apply AI to your own role
- Maintain a sustainable personal AI practice
- Recognize that AI-tool choice is low-stakes and reversible
- Identify the main AI platforms and who makes them
- Explain what all AI platforms have in common
- Compare the strengths of the major AI platforms
- Decide when free vs paid AI is appropriate
- Apply data-safety rules when choosing a platform
- Recognize AI built into existing business tools
- Use a decision framework to choose a platform
- Run a fair head-to-head comparison of AI tools
- Explain why prompting is the core AI skill
- Structure a prompt with role, task, context, format, and example
- Improve results by giving the AI an example
- Control an AI output's format, length, and tone
- Refine AI output through iteration
- Use a role to sharpen AI output
- Apply prompt patterns for common office tasks
- Build and reuse a personal prompt library
- Avoid common prompting pitfalls
Applied AI
200 40 competenciesHands-on fluency with the major platforms β Claude, ChatGPT, Copilot and Gemini β applied to real business work.
- Explain what Claude is and where it fits
- Assess Claude's strengths and weaknesses
- Access Claude across web, desktop, mobile, and browser
- Distinguish the desktop app's Chat, Cowork, and Code modes
- Delegate multi-step work to Claude using Cowork
- Organize and create with Projects, Artifacts, and Skills
- Ground Claude with files, web, research, and connectors
- Use Claude on mobile and via Dispatch
- Describe what Claude Code is and who it's for
- Choose the right Claude plan and model
- Explain what ChatGPT is and where it fits
- Assess ChatGPT's strengths and weaknesses honestly
- Access ChatGPT across web, desktop, mobile, and the Atlas browser
- Choose the right model from the picker (Instant, Thinking, Pro)
- Find, use, and build GPTs via the GPT Store
- Organize work with Canvas, Projects, and Memory
- Do real work with data analysis, files, and image generation
- Use voice, vision, and search effectively
- Delegate multi-step tasks with ChatGPT agent
- Choose the right ChatGPT plan and understand API billing
- Explain what Microsoft Copilot is and where it fits
- Tell the Copilot products apart (free vs paid, consumer vs work)
- Assess Copilot's strengths and weaknesses honestly
- Access Copilot across web, Windows, mobile, in-app, and Edge
- Use Copilot inside Word, Excel, PowerPoint, Outlook, and Teams
- Explain Microsoft Graph grounding and its permission model
- Use agents, Cowork, and Copilot Studio
- Describe the models behind Copilot (GPT and Claude)
- Choose the right Copilot plan and understand licensing
- Apply Copilot well with good governance and data hygiene
- Explain what Google Gemini is and where it fits
- Assess Gemini's strengths and weaknesses honestly
- Access Gemini across web, mobile, Workspace, Chrome, and desktop
- Choose the right Gemini model (Flash, Pro, Deep Think)
- Use Gemini inside Google Workspace (Gmail, Docs, Sheets, Slides, Meet)
- Create with Gemini's multimodal tools (images, video, Gemini Live)
- Use Deep Research, Canvas, and Gems
- Delegate multi-step tasks with Gemini Agent
- Choose the right Gemini plan and the Google One bundle
- Apply Gemini well across the Google ecosystem
Advanced AI
300 22 competenciesPower-user, builder and executive skills: automation, APIs, agents, evaluation, governance, cost and ROI.
- Make a real Claude API call and read the request and response
- Control API cost with caching, batch, and model tiers
- Build a simple agent on the API
- Build an MCP server
- Evaluate and test AI outputs
- Govern AI use: security, privacy, and compliance
- Separate AI hype from reality and lead the AI agenda
- Calculate the true total cost of ownership of AI
- Decide where to augment vs. replace work with AI
- Build a credible, defensible AI ROI case
- Identify the AI security risks executives own
- Establish a corporate AI policy and training program
- Provide effective AI oversight and accountability
- Build and execute an executive AI action plan
- Think in repeatable AI systems, not one-off chats
- Write reusable instructions and config files
- Build a custom AI assistant
- Build reusable skills, commands, and a prompt library
- Connect AI to your tools and data without code
- Automate multi-step work with human oversight
- Do advanced document, data, and multimodal work
- Apply AI to real projects and measure the results
Safe & Responsible AI Use
Safety 10 competenciesThe end-user AI-safety strand: protect data and privacy, classify what is safe to share, and keep a human in the loop.
- Recognize personal responsibility for safe AI use
- Explain how AI tiers use, retain, and train on data
- Classify data green/yellow/red before sharing
- Secure AI accounts and sessions
- Configure data-control settings on major AI tools
- Redact and anonymize inputs before sharing
- Route AI adoption through approved channels
- Recognize AI-enabled phishing, deepfakes, and prompt injection
- Verify AI output before trusting it
- Produce and follow a personal safe-use checklist
AI in the Real World
46 competencies- Reading the exposure & impact estimates
- Task-vs-occupation exposure analysis
- The displacement vs productivity lens
- Growth-claim skepticism
- Following the gains (labor share & concentration)
- The demand paradox
- Income-vs-jobs framing
- Evaluating redistribution designs
- Using historical analogies honestly
- Scenario reasoning
- Transformed, not terminated
- Is my job exposed
- The durable human edge
- Working WITH AI
- Reskilling and upskilling
- AI-resilient and emerging careers
- The portfolio career
- An adaptable mindset
- Future-proofing action plan
- The new rules of deception
- Voice clones and emergency scams
- Deepfake video and authority fraud
- Deepfake images and fading tells
- AI phishing, romance and investment scams
- Sextortion and non-consensual imagery
- Locking down accounts and data
- The verification habit
- Respond, report and recover
- Your money and the one big rule
- Budgeting with AI
- Decoding money jargon with AI
- Smart shopping with AI
- A business plan and side-income
- Solopreneur productivity
- AI is not your financial advisor
- Money scams in the AI era
- Your AI-and-money playbook
- The hidden footprint
- The power bill: training and inference
- Carbon and the honest counter-fact
- Water, land and the neighbours
- Hardware, minerals and e-waste
- The Jevons trap
- Data centers in space
- Night sky, astronomy and Kessler
- Weighing the tradeoffs
See the framework in action
Browse the catalog and watch each course rate your team against real, named AI skills.