Demystifying LLM-based Architectures: A New Guide from LangChain and DataStax

We’ve been working closely with our friends at LangChain, who have developed a powerful, open source framework designed to streamline AI application development. We rely on LangChain as the foundation for our retrieval-augmented generation solution RAGStack (here’s an explanation of why), and we’ve been shoulder-to-shoulder with them on a variety of other cool projects (check out this RAG app we recently built with LangChain on Wikipedia data).

We’re excited to continue our close collaboration with a new, detailed roadmap for leveraging LLMs in production.  

“An LLM Agent Reference Architecture” provides clear and comprehensive guidance to help demystify LLM-based systems. This new guide includes: 

• Common design patterns and use cases that commonly crop up when building generative AI applications.
• In-depth architectural examples (building a chatbot on your documentation, for example)
• A comprehensive look at important considerations to keep in mind when architecting LLM-based systems. 

We hope this guide provides valuable insights and a clear perspective on navigating important architectural considerations that arise when working with LLMs.  Download “An LLM Agent Reference Architecture” today.