This is the Github repository of the coding notebooks from the Coursera course Building and Deploying Generative AI Models from the specialization Generative AI Fundamentals by Alberta Machine Intelligence Institute (Amii).
This repo contains the coding notebooks for Module 2 of the course, where you build, refine, and evaluate Retrieval-Augmented Generation (RAG) systems and compare them with fine-tuning approaches. The notebooks are designed to follow along with the videos, but you can also use them independently to experiment with different RAG techniques.
| Video Title | Coding Notebook |
|---|---|
| Building a Minimal RAG from Scratch with Ollama (Part 1) | 3.2.1.2 Ollama_RAG.ipynb |
| Building a Minimal RAG from Scratch with Ollama (Part 2) | 3.2.1.3 Ollama_RAG_simple_eval.ipynb |
| An Improved RAG Pipeline with LangChain | 3.2.1.4 LangChain_RAG_simple_eval.ipynb |
| Implementing RAG Evaluation | 3.2.1.6 LangChain_RAG_Eval_RAGAS.ipynb |
| Video Title | Coding Notebook |
|---|---|
| Document Loaders and Chunking Strategies | 3.2.2.1 LangChain_RAG_RecursiveSplitter.ipynb |
| Reranking and Contextual Compression | 3.2.2.3 LangChain_RAG_Reranker.ipynb |
| Query Transformation | 3.2.2.4 LangChain_RAG_RePhraser.ipynb |
| Pick the Right Models for your RAG | 3.2.2.5 LangChain_RAG_Final.ipynb |
Clone this repo:
git clone https://github.com/anna1995d/Building-and-Deploying-Generative-AI-Models-RAG-Notebooks.git
cd Building-and-Deploying-Generative-AI-Models-RAG-Notebooks- Python 3.9+
ollamalangChainragas
MIT License Copyright (c) 2025 Anahita Doosti