NoteGenius
You will learn to wire a complete RAG pipeline in LlamaIndex—from document ingestion and vector indexing to grounded question-answering with source attribution. You will gain practical experience using function calls to enable an LLM agent to take autonomous actions and structure prompts with templates to produce reliable, repeatable results. You will also develop the skills to parse documents into nodes, run targeted synthesis workflows—summarization, key-point extraction, text improvement, and translation—and choose the right response mode for each task. This will equip you with the core building blocks for designing LLM-powered applications that can load, reason over, and act on user data.
JetBrains Academy
About
In this project, you'll build an interactive CLI assistant that turns a loose folder of Markdown notes into a searchable, AI-powered knowledge base. It loads your documents, indexes them in a vector store, and lets you ask natural-language questions that return answers and source citations. Beyond retrieval, it can generate brand-new notes from a topic prompt, then summarize, extract key points, improve, or translate any existing note, saving every result back to disk through function calling. By the time you're done, you'll have a self-contained terminal tool that reads, writes, and reasons over your personal notes.
Graduate project
This project covers the core topics of the Building AI Applications with Foundation Models course, making it sufficiently challenging to be a proud addition to your portfolio.
At least one graduate project is required to complete the course.