Building RAG-based LLM Applications for Production (Part 1)
In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale, evaluation and routing.
Building RAG-based LLM Applications for Production Home Blog Blog Detail Building RAG-based LLM Applications for Production By Goku Mohandas and Philipp Moritz | October 25, 2023 Check out our updated RAG blog For the most up-to-date content on how to run the best RAG pipelines with Ray, read our updated blog . [ GitHub | Notebook | Anyscale Endpoints | Ray Docs ] · 55 min read Note: Check out the new evaluation reports and cost analysis with mixtral-8x7b-instruct-v0.1 and our data flywheel workflow to continuously improve our RAG applications. In this guide, we will learn how to: 💻 Develop a
Explore this link on the map →related reading
- Patterns for Building LLM-based Systems & Productseugeneyan.com
- What is Retrieval Augmented Generation (RAG)? | Databricksdatabricks.com
- Retrieval-Augmented Generation for Large Language Models: A Surveyarxiv.org
- RAG Architecture Deep Divelinkedin.com
- A beginner's guide to building a Retrieval Augmented Generation (RAG) application from scratchlearnbybuilding.ai
- Towards Data Sciencetowardsdatascience.com
- Advanced RAG Techniques: What They Are & How to Use Themfalkordb.com
- 10 Ways to Improve the Performance of Retrieval Augmented Generation Systems | Towards Data Sciencetowardsdatascience.com
- Building Performant RAG Applications for Production | Developer Documentationdocs.llamaindex.ai
- RAG Chunk Size Guide: Find The Best Setting | LlamaIndexblog.llamaindex.ai
- Scaling RAG from POC to Production | Towards Data Sciencetowardsdatascience.com
- 10 Ways to Improve the Performance of Retrieval Augmented Generation Systems | Towards Data Sciencetowardsdatascience.com