relearnable.ai

Overview

relearnable.ai is a full-stack AI learning SaaS I designed and deployed to help users genuinely retain what they learn. The core innovation is a Knowledge Correction Engine—an LLM-driven system that identifies gaps in understanding and generates targeted curriculum in real time.

Problem

Most learning platforms measure completion, not comprehension. Users finish courses but forget the material weeks later. There's no feedback loop that catches misconceptions early or adapts content based on what a learner actually struggles with.

Constraints

• System needed to support live users at production scale
• LLM responses had to be fast enough for real-time interaction
• Mobile-first UX with minimal friction for repeat sessions
• Curriculum logic needed to be flexible across subjects

Solution

I built a Knowledge Correction Engine that uses LLM-generated assessments (powered by Google Gemini) to detect where learners have misconceptions. When gaps are identified, the system dynamically generates targeted curriculum trees—structured learning paths that rebuild mastery from the point of confusion. Instead of restarting learners from the beginning, curriculum starts precisely at the point of misunderstanding.

Architecture & Stack

Key Engineering Decisions

• Structured LLM outputs with schema validation for reliable curriculum generation
• PostgreSQL relational modeling for curriculum trees and prerequisite tracking
• Row-Level Security for user data isolation without custom auth infrastructure
• Component memoization and hardware-accelerated CSS for 50%+ load time improvement
• Mobile-first responsive design optimized for repeat engagement

Challenges & Tradeoffs

The main challenge was ensuring LLM-generated curriculum felt intentional rather than random. I solved this by designing a tree-based curriculum schema where each node has prerequisite relationships. When the correction engine detects a gap, it walks up the tree to find the true root cause, then generates targeted content from that point. The tradeoff: more complex state management, but significantly more effective curriculum delivery.

Results

What This Demonstrates

• Full-stack ownership from system design to production deployment
• AI system design beyond API usage—structured outputs, validation, and failure handling
• Performance optimization with measurable outcomes
• Product thinking: solving real user problems, not just building features