Building Context-Aware Reasoning Applications with LangChain and LangSmith

How can companies best build useful and differentiated applications on top of language models? Many of the products and companies built do this by providing the relevant context to LLMs and asking it to reason appropriately. In this talk, Harrison will discuss the different types of context you should be aware of, the different levels of cognitive architectures that are emerging, and how LangChain and LangSmith are built to help with this journey.

In this session, Harrison will walk through common problems in evaluating context-aware reasoning applications. He will then talk about how LangSmith - a platform for managing these types of applications - helps address some of these issues, as well as other practical techniques you can implement to tackle this.

About Harrison:
Harrison Chase is the co-founder and CEO of LangChain, a company formed around the open-source Python/Typescript packages that aim to make it easy to develop Language Model applications. Prior to starting LangChain, he led the ML team at Robust Intelligence (an MLOps company focused on testing and validation of machine learning models), led the entity linking team at Kensho (a fintech startup), and studied stats and CS at Harvard.

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#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai