# Enabling LLM-Powered Applications with Harrison Chase of LangChain

Page: https://stenobird.com/podcast/gradient-dissent/enabling-llm-powered-applications-with-harrison-chase-of-langchain
Text version: https://stenobird.com/podcast/gradient-dissent/enabling-llm-powered-applications-with-harrison-chase-of-langchain.md
Podcast: [Gradient Dissent: Conversations on AI](https://stenobird.com/podcast/gradient-dissent)
Published: 2023-06-01T05:20:00+00:00
Episode link: https://wandb.ai/site/resources/podcast
Audio file: https://podcasts.captivate.fm/media/f119eaf8-5c3b-44d9-8db8-cfe89a96e71a/WEIGHTS-Harrison-Chase-V2.mp3
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JSON: https://stenobird.com/v1/public/podcasts/gradient-dissent/episodes/enabling-llm-powered-applications-with-harrison-chase-of-langchain
Duration seconds: 3114

## Resource

On this episode, we’re joined by Harrison Chase , Co-Founder and CEO of LangChain . Harrison and his team at LangChain are on a mission to make the process of creating applications powered by LLMs as easy as possible. We discuss: - What LangChain is and examples of how it works. - Why LangChain has gained so much attention. - When LangChain started and what sparked its growth. - Harrison’s approach to community-building around LangChain. - Real-world use cases for LangChain. - What parts of LangChain Harrison is proud of and which parts can be improved. - Details around evaluating effectiveness in the ML space. - Harrison's opinion on fine-tuning LLMs. - The importance of detailed prompt engineering. - Predictions for the future of LLM providers. Resources: Harrison Chase - https://www.linkedin.com/in/harrison-chase-961287118/ LangChain | LinkedIn - https://www.linkedin.com/company/langchain/ LangChain | Website - https://docs.langchain.com/docs/ Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation. #OCR #DeepLearning #AI #Modeling #ML

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## Transcript

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