Practical Data Considerations for Building Production-Ready LLM Applications

Large Language Models (LLM’s) are starting to revolutionize how users can search for, interact with, and generate new content. Some recent stacks and toolkits around Retrieval Augmented Generation (RAG) have emerged where users are building applications such as chatbots using LLMs on their own private data. This opens the door to a vast array of applications. However while setting up a naive RAG stack is easy, there is a long-tail of data challenges that the user must tackle in order to make their application production-ready. In this talk, we give practical tips on how to manage data for building a scalable/robust/reliable LLM software system, and how LlamaIndex + Ray provide the tools to do so.
Find the slide deck here: drive.google.com/file/d/1n0SC...
About Anyscale
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Anyscale is the AI Application Platform for developing, running, and scaling AI.
www.anyscale.com/
If you're interested in a managed Ray service, check out:
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About Ray
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Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads.
docs.ray.io/en/latest/
#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai

[@anantwag19]

That initial 4 lines of code for Reading lot of pdf documents and creating index really resulted in Hallucination and non accurate answers .