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Enabling LLM-Powered Applications with Harrison Chase of LangChain
Manage episode 364934469 series 2973389
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
125 قسمت
Manage episode 364934469 series 2973389
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
125 قسمت
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