- standardization, governance, simplified troubleshooting, and reusability in ML application development.
- integrations with vector databases and LLM providers to support new applications -
provides tutorials on integrating
This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
LangChain has many advanced retrieval methods to help address these challenges. (1) Multi representation indexing: Create a document representation (like a summary) that is well-suited for retrieval (read about this using the Multi Vector Retriever in a blog post from last week). (2) Query transformation: in this post, we'll review a few approaches to transform humans questions in order to improve retrieval. (3) Query construction: convert human question into a particular query syntax or language, which will be covered in a future post
Bookmarks on this page are managed by an admin user. 