kevintsai/Building-and-Evaluating-Advanced-RAG-Applications
Jupyter notebooks for course Building and Evaluating Advanced RAG Applications, taught by Jerry Liu (Co-founder and CEO of LlamaIndex) and Anupam Datta (Co-founder and chief scientist of TruEra).
This project helps AI practitioners and data scientists refine and assess their Retrieval Augmented Generation (RAG) systems. It provides practical examples and methods to improve how information is found and used by AI, ultimately leading to more accurate and reliable AI responses. You'll go from a basic RAG setup to an advanced, production-ready system.
No commits in the last 6 months.
Use this if you are building or maintaining AI applications that use RAG and want to ensure they provide accurate, relevant, and truthful information.
Not ideal if you are looking for an introduction to the very basics of large language models or prompt engineering without a focus on RAG implementation and evaluation.
Stars
51
Forks
21
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 06, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/kevintsai/Building-and-Evaluating-Advanced-RAG-Applications"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
bragai/bRAG-langchain
Everything you need to know to build your own RAG application
guyernest/advanced-rag
Jupyter Notebooks for Mastering LLM with Advanced RAG Course
liu673/rag-all-techniques
Implementation of all RAG techniques in a simpler way（以简单的方式实现所有 RAG 技术）
FareedKhan-dev/rag-ecosystem
Understand and code every important component of RAG architecture
FareedKhan-dev/14-rag-failures
Encountering 14 different Naive RAG fails and using KG to solve it