Reducing Hallucinations in LLMs | Retrieval QA w/ LangChain + Ray + Weights & Biases 

Discover how to construct an LLM-based question and answering (QA) service that combats hallucinations using Retrieval QA techniques. This tutorial introduces Ray, LangChain, and Weights and Biases as essential tools for building a powerful QA system. Ray enables efficient distributed computing, while LangChain provides a language modeling platform for handling complex queries. Weights and Biases aids in model observability.
Step by step, learn how to set up the infrastructure, integrate the tools, and train your LLM model. Explore the power of Retrieval QA, leveraging search engines to reduce hallucinations and enhance answer accuracy. Code snippets, demos, and optimization tips are shared. Subscribe now and get started!
Learn More
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Blog Post: www.anyscale.c...
Code: github.com/ray...
LangChain Docs: python.langcha...
Ray Docs: docs.ray.io/en...
Ray Overview: www.ray.io/
Join the Community!
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Twitter: / raydistributed
Slack: docs.google.co...
Discuss Forum: discuss.ray.io/
Managed Ray
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If you're interested in a managed Ray service, check out: www.anyscale.c...
#llm #machinelearning #langchain #ray #gpt #chatgpt