LangChain with Golang: Building Workflows with Chains

Description

Get the full course here: https://www.langcasts.com/courses/langchain-golang Go developers, it's time to build structured, multi-step AI applications! While you've mastered single LLM calls, real-world services require coordinating multiple components—from prompt formatting to data retrieval and parsing. This is where Chains in LangChainGo become essential. In this deep-dive tutorial, we explore LangChain with Golang: Building Workflows with Chains. You'll learn how to sequence LLMs, prompt templates, and output parsers into unified pipelines, transforming simple API calls into robust, end-to-end AI workflows. We'll cover both Sequential Chains and the concepts behind more Dynamic Workflows that adapt to user input. Mastering chains in Go allows you to move beyond basic prototypes and build scalable, reliable AI microservices that leverage the performance of Golang. 🔥 Ready to build scalable, multi-step AI workflows? Master Chains in LangChainGo! 👍 Found this technical guide helpful? Please give it a like! 👇 What's the first complex, multi-step chain you plan to build with Go? Share in the comments! 🚀 Don't miss future videos on Golang backend development, advanced LangChain patterns, and production deployment! Subscribe now! 🔗 Share this video with fellow Go developers looking to structure their AI code! #LangChainGolang #GoLLM #GolangAI #go_langchain #LangChainGoTutorial #Chains #LCEL #SequentialChain #LLMWorkflow #AICoding #TechTutorial #GoConcurrency #LangChain