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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But... show more

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Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by Zain, experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

Andrew Ng

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63,773 görüntüleme • 6 ay önce

📊 Learn how to observe & evaluate agents on LangChain Academy 📊 Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave. Add multi-turn interactions and tool-calling agents, and testing agents becomes even more complex than traditional software testing 😵‍💫 To address this challenge, LangSmith provides a comprehensive platform for agent engineering that helps teams use live production data for continuous testing and improvement 🚀 In our quickstart course, LangSmith Essentials, you’ll learn how to observe, evaluate, and deploy your agent in less than 30 minutes. Enroll for free ➡️

📊 Learn how to observe & evaluate agents on LangChain Academy 📊 Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave. Add multi-turn interactions and tool-calling agents, and testing agents becomes even more complex than traditional software testing 😵‍💫 To address this challenge, LangSmith provides a comprehensive platform for agent engineering that helps teams use live production data for continuous testing and improvement 🚀 In our quickstart course, LangSmith Essentials, you’ll learn how to observe, evaluate, and deploy your agent in less than 30 minutes. Enroll for free ➡️

LangChain

13,518 görüntüleme • 6 ay önce

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

Big Pharmai

58,542 görüntüleme • 1 yıl önce

With the launch of GLM-5, introduces Agent Mode. - Agent Mode: Automatically breaks down tasks, orchestrates tools, drives execution, and delivers ready-to-use files. - Data Insights & Smart Writing: Upload data for instant visualizations. Go from outline to finished draft, all in one place. - AI Slides / Full-Stack upgraded: Now handles more complex instructions and multi-step workflows.

With the launch of GLM-5, introduces Agent Mode. - Agent Mode: Automatically breaks down tasks, orchestrates tools, drives execution, and delivers ready-to-use files. - Data Insights & Smart Writing: Upload data for instant visualizations. Go from outline to finished draft, all in one place. - AI Slides / Full-Stack upgraded: Now handles more complex instructions and multi-step workflows.

Z.ai

29,781 görüntüleme • 4 ay önce

Discover how AI agents are revolutionizing data utilization. In this clip, Anjali George from Theoriq showcases how multi-agent systems unlock untapped data reservoirs, enabling faster insights, smarter decisions, and innovative breakthroughs with Filecoin's ecosystem.

Discover how AI agents are revolutionizing data utilization. In this clip, Anjali George from Theoriq showcases how multi-agent systems unlock untapped data reservoirs, enabling faster insights, smarter decisions, and innovative breakthroughs with Filecoin's ecosystem.

Filecoin

19,579 görüntüleme • 1 yıl önce

Learn about our research prototype LLM-powered personal health agent that analyzes various data modalities, including data from wearable devices, to offer evidence-based health insights and to provide a personalized coaching experience. Read more →

Learn about our research prototype LLM-powered personal health agent that analyzes various data modalities, including data from wearable devices, to offer evidence-based health insights and to provide a personalized coaching experience. Read more →

Google Research

25,348 görüntüleme • 9 ay önce