AI Engineering Academy provides structured learning paths for applied AI concepts, with emphasis on prompt engineering, RAG, and fine-tuning. It has substantial GitHub activity and a MIT license.
Collecting history — the radar snapshots this repo daily. The trend line appears after 3 days of data (1 so far).
What it is
AI Engineering Academy is a repository aimed at structuring learning paths for applied AI concepts, including Prompt Engineering, Retrieval Augmented Generation (RAG), and Fine-tuning, with sections for projects, deployment, and community involvement.
How it works
The repository organizes content into learning paths, each containing modules and topics such as core concepts, implementation details, production considerations, and hands-on projects. It includes a Getting Started flow that guidance users to choose a path, follow modules in order, practice exercises, build projects, and share within the community.
Getting started
The README indicates the following steps under Getting Started:
"1. Choose Your Path: Select a learning track that matches your goals" "2. Follow the Structure: Complete modules in the recommended order" "3. Practice: Implement the concepts through provided exercises" "4. Build: Create your own projects using the knowledge gained" "5. Share: Contribute to the community and help others learn"
Recent releases
No releases are listed under RELEASES (latest 0): - none
Traction
Stars: 2368 (as per repository metadata). Open issues: 7. Language: Jupyter Notebook. License: MIT.
Behind the repo
Maintainer: Adithya S Kolavi (GitHub handle shown in the README). The repo is associated with CognitiveLab and AI-focused learning content, with community contributors and a star history visualization linked in the README.
Caveats
License: MIT. Created: 2023-10-05. Last push: 2026-02-27. Topics include fine-tuning, finetuning, finetuning-llms, inference, large-language-models, llm, python, quantization. No explicit release entries are provided in the latest releases section.