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RAG / EdTech · EdTech

Trainizi: AI Course Builder for Nano-Learning

Document-to-course platform for corporate training

Challenge

Turn unstructured source documents into structured, teachable mini-courses without a human rewriting every one by hand.

My role

Six-month build: the RAG pipeline that parses documents into course structure, plus student dashboards and progress tracking.

Evidence

Around 10,000 learners and roughly 5,000 generated mini-courses.

Corporate training material already exists, but it exists as PDFs, slide decks and policy documents that nobody works through voluntarily. Trainizi turns that material into nano-learning: mini-courses of about a minute each, which people will actually finish.

The core is a RAG pipeline rather than a single prompt. Source documents are parsed and chunked, embedded into a vector store, then retrieved against as course structure is generated, so the output stays anchored to what the document actually says instead of drifting into plausible-sounding invention. Getting chunking and retrieval right mattered more to output quality than any amount of prompt tuning.

Around that I built the learner side: student dashboards, progress tracking, and the flow that moves someone through a generated course. A generated course nobody can navigate is not a product.

Over the six-month build the platform reached roughly 10,000 learners and about 5,000 generated mini-courses.

Outcomes

  • Around 10,000 learners on the platform
  • Roughly 5,000 mini-courses generated from source documents
  • Six-month build covering pipeline, dashboards and progress tracking

Stack

RAG pipelineVector searchOpenAI APIPythonNext.js

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