Use annotation queues | 🦜️🛠️ LangSmith
Annotation queues are a powerful LangSmith feature that provide a streamlined, directed view for human annotators to attach feedback to specific runs.| docs.smith.langchain.com
Annotation queues are a powerful LangSmith feature that provide a streamlined, directed view for human annotators to attach feedback to specific runs.| docs.smith.langchain.com
These guides answer “How do I…?” format questions.| docs.smith.langchain.com
The quality and development speed of AI applications is often limited by high-quality evaluation datasets and metrics, which enable you to both optimize and test your applications.| docs.smith.langchain.com
This conceptual guide covers topics that are important to understand when logging traces to LangSmith. A Trace is essentially a series of steps that your application takes to go from input to output. Each of these individual steps is represented by a Run. A Project is simply a collection of traces. The following diagram displays these concepts in the context of a simple RAG app, which retrieves documents from an index and generates an answer.| docs.smith.langchain.com
Build language agents as graphs| langchain-ai.github.io
To hear directly from the authors on this topic, sign up for the upcoming virtual event on June 20th, and learn more from the Generative AI Success Stories Superstream on June 12th.| O’Reilly Media
How to construct domain-specific LLM evaluation systems.| hamel.dev
Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver. Agent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:| lilianweng.github.io
