Announcing our newest GenAI course: RAG++
Explore advanced RAG techniques like hybrid search and LLM context management through a case study and hands-on exercises. Included in the course are code notebooks and Cohere credits to build and experiment with your own RAG systems.
Created on September 17|Last edited on September 23
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We're excited to announce our latest course, RAG++. We designed it to give you the practical skills you need to build and deploy production-ready retrieval augmented generation (RAG) systems for real-world LLM applications. And like all our courses, it's completely free.
Register now
Led by industry experts from Weights & Biases, Cohere, and Weaviate, this course begins with the fundamentals of RAG and builds up to creating you own sophisticated RAG system. You'll learn how to effectively evaluate RAG performance, optimize retrieval strategies, craft advanced prompts, and build cost-efficient and scalable LLM applications. Here's a high level look at the chapters:
RAG techniques covered
Our RAG++ course gives you the essential skills needed to build high-performing RAG systems. You'll learn about:
- Robust evaluation strategies: Accurately measure your system's effectiveness and identify areas for improvement.
- Efficient data preprocessing techniques: Optimize retrieval speed and accuracy through techniques like chunking and metadata management.
- Advanced query understanding: Utilize methods like query decomposition to ensure your system accurately captures user intent.
- Hybrid search and vector databases: Leverage tools like Weaviate to optimize retrieval performance and scalability.
Utilize ready course notebooks implemented with Cohere's cutting-edge language AI platform and benefit from Cohere credits to build and test your own RAG systems.
Case study
Learn directly from the creators and maintainers of Wandbot, Weights & Biases' own chatbot, as they share their firsthand experience building a real-world RAG system. This in-depth case study provides a practical lens, examining Wandbot's design choices, evaluation metrics, and iterative improvements. Gain valuable insights into the challenges and solutions encountered when building and deploying a production-ready RAG system, providing you with a roadmap for your own RAG projects.
We're thrilled with how the course came together and we'd love to hear your feedback. You can take the RAG++ at your own pace and, of course, it's completely free.
Enroll now!
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