Use-Cases with LangSmith and LangChain
In the world of AI, Large Language Models (LLMs) are revolutionizing the way we interact with technology. From chatbots to AI dungeon masters, the applications are endless. However, the journey from prototype to production can be a challenging one. Enter LangSmith and LangChain, two platforms designed to streamline the development of LLM-powered applications. In this blog post, we'll explore some of the unique use-cases that can be created using these innovative tools.
Streamlit and Snowflake, two data-centric companies, have used LangSmith to develop intelligent agents that can answer questions about their data. By providing full visibility into model inputs and outputs, LangSmith allows developers to experiment with new chains and prompt templates, making it easier to spot the source of unexpected results, errors, or latency issues. This deep visibility into model performance has been instrumental in helping these companies prototype intelligent agents.
Boston Consulting Group (BCG) has built a series of highly-customized, high-performance applications on top of LangChain’s framework. By using LangSmith, BCG was able to quickly set up an evaluation pipeline, making it easier to trace and evaluate complex agent prompt chains. This reduced the time required to debug and refine their prompts, enabling them to move swiftly to deployment.
In partnership with DeepLearningAI, LangSmith has been used to equip learners in the recently-released LangChain courses. This allowed students to easily visualize the exact sequence of calls, and the inputs and outputs at each step in the chain. This hands-on approach helps build intuition as students learn to create new and more sophisticated applications.
Klarna, a leading fintech company, is using LangSmith to build industry-leading AI integrations. By focusing on a specific section of their application, LangSmith provides the tools and data they need to ensure no regressions occur, helping them to maintain a high-quality user experience.
Startups like Mendable, Multi-On, and Quivr are using LangSmith to actively track and optimize the performance of their applications. By providing insights into system-level performance, model/chain performance, and user interactions, LangSmith enables these startups to take action on critical issues and continuously improve their applications.
Fintual, a Latin American startup, is using LangSmith to build a personalized financial advisor. Given the high bar for accuracy, personalization, and security in financial products, LangSmith helps Fintual build products they are confident putting in front of users.
In conclusion, LangSmith and LangChain are not just tools, but catalysts for innovation in the world of LLM-powered applications. Whether it's developing intelligent agents for data analysis, creating customized applications for business consulting, or building a personalized financial advisor, these platforms are empowering developers to push the boundaries of what's possible with LLMs.
Have some interesting use-cases you'd like to share or show off? Contact Eric David Smith to share and discuss ideas.
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