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, 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.
