The Problem
100,000+ people "learn AI" every month, but less than 10% can ship production code.
Employers can't tell who can build vs who watched tutorials.
The Solution
I built Forge AI — a skill verification pipeline that replaces courses with real engineering work.
Here's how it works:
1. Real Engineering Tickets
Users get 5 real RAG tickets from a fictional company (Meridian AI):
- RAG-114: Chunking returns empty results on long documents
- RAG-115: Hybrid score fusion produces incorrect rankings
- RAG-116: Metadata filter ignores date conditions
- RAG-117: Reranker returns raw scores instead of final documents
- RAG-118: Faithfulness evaluator always returns 0%
2. Write Code in a Real Editor
The workspace has:
- A task description panel (the ticket)
- A Monaco code editor with starter code
- A terminal panel that shows results
3. Automated Evaluation
When a user submits code:
- It runs in a sandbox (timeout + memory cap)
- Pytest suites check correctness
- An AI judge (Groq LLM) provides detailed feedback
4. Verified Skill Profile
Users get a talent graph showing scores across 5 competencies:
- Chunking
- Fusion
- Filtering
- Reranking
- Evaluation
The Tech Stack
- Frontend: Next.js 16.2.10, React 19, TypeScript, Tailwind CSS 4
- Editor: @monaco-editor/react
- Backend: Python 3.13, FastAPI
- Database: Supabase (PostgreSQL)
- Auth: Supabase Auth
- AI Judge: Groq API (llama-3.3-70b-versatile)
- Hosting: Vercel (frontend), Render (backend)
The Architecture
The backend pipeline is a plain Python function composition:
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