Decoding AI
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Episode
8
Building LLM Applications Using Langchain
In a recent Decoding AI webinar, Prayank Swaroop from Accel and Lance Martin from LangChain discussed using LangChain for building powerful LLM applications. By chaining multiple LLMs together, LangChain empowers developers and businesses to leverage latest breakthroughs in AI language processing. LangSmith complements LangChain by helping debug and refine these LLM apps. The webinar highlighted six best practices for building LLM apps, including utilizing LangChain's Agent Framework for collaboration among language models, integrating real-time data streams with LangSmith, and addressing hallucinations and text length challenges. By leveraging LLMs and LangChain, developers can create dynamic AI applications with human-like text generation capabilities.
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We’re building Passionfroot 1, an operating system for creators to manage their business.
We’re based in Europe and raised our $ 3.4m pre-seed round from Creandum and US angels such as Vlad (Webflow) and creators like Ali Abdaal.As we’re creating a new category and as the creator economy is global and mostly online, we’re building from Day 1 a global company and product that helps creators monetize around the world.
This brings a lot of complexities especially in terms of payments and taxes as our early users are based both in Europe and the US and deal with cross-border transactions.What are Do’s and Dont’s for European startups who have a global ambition and build products for a global customer base from Day 1. Anything you would do differently?
Thanks @pjbouten! Love how you’ve thought about incrementally shaping a category with a focus on product and service, and only then thinking about getting the message out there vs the other way around.
And totally agree on the distinctions and overlaps both self-serve and enterprise motions share.
Thanks, PJ, for taking the time to address questions from the great vantage of shaping Showpad into a global, enterprise SaaS brand!