AI Power User: 0 → 1
Turn your 3-month gap into income, skills, and a real portfolio before college even starts. This isn't another “learn AI tools” course — it's a system for producing builders who ship.
Build. Break. Fix. Ship.
8 weeks. 14 modules. Real projects. Real users. A portfolio that actually proves you can deliver.
WHILE
U LEARN
“Learn tools and build projects.”
Solve real problems with AI systems that actually work under constraints.
Every module follows 4 non-negotiable steps
Skip any one and you don't pass. That's how we make sure you actually learn — not just consume.
Ship something real every module — no toy demos.
Stress-test it. Find where AI quietly fails.
Improve reliability with constraints & evals.
Prove someone actually wants what you built.
14 Modules. Zero Filler.
Modules marked with ⚡ are the upgrades that separate amateurs from people who actually ship AI systems in production.
2-Hour Reality Check
Destroy naive thinking early. What LLMs can and can't do, why 80% of AI apps fail.
Build a Daily AI Brief → break it → fix with constraints.
AI Ecosystem (Rebuilt)
Closed vs open models. API vs local. Latency / cost / quality triangle. Decision frameworks.
Same task → 3 models → justify your stack like an engineer.
How LLMs Actually Work
Token prediction, hallucinations, context poisoning, instruction hierarchy, determinism vs randomness.
Write a prompt that fails at high temp, succeeds at low.
Prompt Engineering (Upgraded)
Prompt compression, defensive prompting, strict JSON schemas. No CoT romance.
Build a Bulletproof Prompt that passes 20 test cases.
Evaluation & Reliability ⚡
Golden datasets, pass/fail scoring, edge cases, adversarial prompts. Stop building demos, start building systems.
30 test cases → score outputs → improve until ≥85% pass rate.
APIs Done Right
Retries, timeouts, fallback models, cost tracking — not just calling an LLM.
Reliable LLM Pipeline with fallback + full logging.
RAG (For Real)
Where RAG breaks, chunking strategies, retrieval errors, measurement.
Build RAG, measure retrieval accuracy, then improve it.
AI System Design ⚡
Pipeline vs agent. Stateless vs stateful. Caching. Cost vs latency tradeoffs.
Design a research assistant. Justify every component.
Agents (De-Hyped)
Agents are expensive, slow, often unnecessary. LangGraph, CrewAI — and when NOT to use them.
Build an agent → replace it with a simpler pipeline.
Local LLMs
When local actually makes sense — privacy, cost at scale. Hands-on with Ollama.
Run a local model. Compare vs API on real tasks.
Build & Ship (Hardened)
Lovable.dev, Replit, Cursor. Not 'just build' — must have users, feedback, iteration.
Ship a tool that solves a real problem with 3+ external users.
AI Product Thinking ⚡
Problem > solution. ROI thinking. Why most AI startups fail.
Interview 5 users → kill one idea → refine another.
Production Mindset
Monitoring, logging, cost dashboards, failure handling.
Stress test your system. Fix the breaking points.
Capstone (Brutal Version)
RAG OR pipeline + evaluation system + real users + measurable success metric.
Live app + case study: problem, design, failures, improvements.
To complete this course, you must have:
Most courses let everyone pass. We don't. Otherwise you didn't learn — you just consumed.
3 shipped tools
1 with real users
1 evaluated properly
1 improved after failure
Learn from people who actually build
Aditya Karki
AI Systems & Engineering Lead
Sagar Gautam
AI Product & Build Mentor
Ready to actually build?
Drop your details and we'll reach out within 24 hours with cohort dates, payment info, and what to prepare. Limited seats per cohort.
- ✓Rs. 1500 total · 8 weeks
- ✓Online + Offline (Baneshwor, Kathmandu)
- ✓Real projects, not toy demos
- ✓Mentorship from working AI builders