AI 101
Getting Better Use Out of AI
A beginner-friendly course on practical LLM use: choosing tools, prompting well, managing context, and building a working stack.
After you save this
getsavv makes completed learning legible: program, date, and the artifact you shipped — shareable proof, not vapor.
Continue with clarity
The saved item should reopen into a clear next step.
Track meaningful progress
Progress should show stages completed and workflows rebuilt.
Capture proof of work
The goal is a visible artifact, not just a saved reference.
Audience
Professionals who want practical AI fluency without a technical deep dive
Format
Self-guided course · 7 modules, 2-3 hours
Level
Beginner
Curriculum
Module 1
Choose your player
Compare Gemini, Perplexity, ChatGPT, Claude, and DeepSeek by job to be done.
Module 2
Prompting fundamentals
Get reliable output through structure, specificity, and iteration.
Module 3
Context matters
Understand context windows and how to carry relevant information forward.
Module 4
Markdown and editors
Use simple text structure and editing tools to work with models more effectively.
Module 5
Version control for AI work
Track prompts, outputs, and experiments without losing your thread.
Module 6
Model Marvel super team
Combine multiple models into a workflow that matches your needs and budget.
Outcomes
- Choose the right model for common tasks
- Write stronger prompts with less trial and error
- Use markdown, editors, and version control in AI workflows
- Build a personal AI tool stack
Academic Foundations
John Sweller · 1988
Cognitive Load During Problem Solving: Effects on Learning
A foundation for designing instruction that avoids overwhelming learners with unnecessary complexity.
Peter C. Brown, Henry L. Roediger III, Mark A. McDaniel · 2014
Make It Stick
Synthesizes learning science around retrieval, spacing, interleaving, and generation for durable skill building.
Journey
How this should turn into capability inside getsavv
Saving a strong source should push the learner through a visible sequence from extraction to proof.
Step 1
Add
Save the cohort, challenge, course, or workshop you want to finish.
Step 2
Plan
Turn the program into a concrete completion checklist with the right next step.
Step 3
Complete
Move from enrolled to in progress to completed with visible momentum.
Step 4
Capture
Attach the artifact, workflow, doc, or demo that proves the work happened.
Step 5
Publish
Share one public proof page showing the program, date, and outcome.
Values
The values that should stay visible while learning from this source
Completion before collection
The product should prioritize finished programs over saved intent.
Proof over certificates
A visible artifact matters more than a badge with no work attached.
Make evidence shareable
Every completed program should be easy to share with recruiters, managers, or instructors.
Keep the next step obvious
Progress should reopen into one concrete action, not a vague reminder to come back later.