Changelog

A new tutorial on embeddings and vector databases is now available. It covers the fundamentals of embeddings and how to use them to build a semantic search engine for your RAG system.

A new tutorial on how to run AI models locally with Ollama is now available. It covers the basics of installing Ollama, downloading a model, and interacting with it through the command line and Python.

A new tutorial on effective chunking strategies is now available. It covers the fundamentals of chunking and what chunking strategies are best for different types of documents.

The Python Essentials module is now updated in line with the rest of the academy. It's now more focused on the practical skills you'll need as an AI engineer.

The first module of the academy is now complete. It covers the basics of setting up your development environment, including the command-line toolkit, Python project setup, and IDE (VScode/Cursor) configuration.

A new tutorial on Retrieval-Augmented Generation (RAG) is now available. It covers the fundamentals of RAG and how to build a simple RAG system from scratch to production-ready API with FastAPI and Docker.

Add a section on how to use a Vision Language Model (VLM) to extract text from images in a document. Using Docling and Ollama (Nanonets-OCR-s).

Use the latest Antropic and OpenAI guidelines for prompt engineering along with a new example.

Added RAG and MCP AI Agent tutorials to the academy. These tutorials have associated GitHub project repositories.