“You can now visualize traces as a waterfall graph for deeper insights into your app’s latency in LangSmith. Use the waterfall graph to: • Spot bottlenecks at a glance • Understand parallel vs. sequential execution • Optimize response times with precision Try it out today:
https://x.com/LangChainAI/status/1884987434645041482
“Beyond coding agents and using them as advanced retrieval systems, I am always on the hunt for new and interesting use cases for these AI models. This is the latest fascinating use case I have come across: LLMs applied to historical research. This is a great piece on how LLMs
https://x.com/omarsar0/status/1883890211538776501
“🤖 Build ChatGPT-Style Bots with LangGraph Create AI chatbots with memory and tool integration using LangGraph’s powerful architecture. This tutorial guides you through building a complete solution with dynamic workflows and conversation memory. Learn more 👉
https://x.com/LangChainAI/status/1883666232789889259
“🤖 How to Evaluate Document Extraction 📃 Document extraction is a common use case for LLMs, transforming unstructured text into structured data. It’s important to evaluate your extraction pipeline’s performance, especially for large-scale or high-stakes applications. In this” / X
https://x.com/LangChainAI/status/1885012449352704123
“Agentic RAG Overview This is a great intro to LLM agents and Agentic RAG. It provides a comprehensive exploration of Agentic RAG architectures, applications, and implementation strategies.
https://x.com/omarsar0/status/1881360794019156362
