flâneur

Partnering with LangChain: The LLM Application Framework | Sequoia Capital

sequoiacap.com · 890 words · saved by 1 readers

We are at the dawn of a new age of applications, with large language models (LLMs) as the enabling technology. But programming an LLM remains more art than science. The instructions that generate robust, reliable performance out of these non-deterministic models remain poorly understood. We are still in a wild west of prompt hacking, eyeballing outputs and a lot of duct tape when it comes to Generative AI. We have seen this movie play out before. In Software 1.0, frameworks such as React and Next have abstracted, modularized and organized the most common building blocks of software engineering, making it fast and reliable to build and test your next mobile app or website. Generative AI, too, deserves a proper programming framework. Developers are craving a common set of best practices and composable building blocks for their LLM applications, to simplify the complex and to avoid reinventing the wheel. LangChain founders Harrison Chase and Ankush Gola have designed just that: an open-so

Partnering with LangChain: The LLM Application Framework Harrison, Ankush and their team are building the AI programming framework every engineer needs. By Sonya Huang Published February 15, 2024 Team LangChain We are at the dawn of a new age of applications, with large language models (LLMs) as the enabling technology. But programming an LLM remains more art than science. The instructions that generate robust, reliable performance out of these non-deterministic models remain poorly understood. We are still in a wild west of prompt hacking, eyeballing outputs and a lot of duct tape when it com

related reading