Learn what pairwise evaluation is, why you might need it for LLM app development, and see an example of how to use it in LangSmith by LangChain.

A guide to building production-ready RAG apps using Pinecone Serverless, LangChain, and LangServe, addressing pain points like vectorstore management, rapid deployment, and observability.

OpenEvals and AgentEvals provide pre-built evaluators for LLM-as-judge, structured data, and agent trajectory evaluation. These open-source packages help developers quickly establish evaluation workflows to ensure reliability of LLM applications.

Learn to build reliable AI agents. Compare workflows vs agents, declarative vs imperative approaches, and why context control matters most.

LangSmith introduces self-improving LLM-as-a-Judge evaluators that leverage human corrections as few-shot examples to align evaluations with human preferences without prompt engineering.

Promptim is an experimental library that automates prompt optimization by iteratively refining prompts using datasets and evaluators, aiming to save time and improve AI system performance.

New Computer used LangSmith to improve their AI memory retrieval system, achieving 50% higher recall and 40% higher precision, by tracking regressions and adjusting conversation prompts.

Learn 5 patterns for evaluating deep agents: bespoke testing, single-step validation, full turns, multi-turn simulations, and environment setup.

Learn how LangChain uses LangSmith LLM Gateway to track coding agent spend in real time, set budgets by team and user, and prevent runaway AI costs.

An Axios piece reveals that personality clashes between Anthropic and the US government led to the shutdown of its AI models (Mythos and Fable) under export controls. Sources suggest solutions include making models jailbreak-proof or improving attitudes.