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Most RAG apps fail due to retrieval. Today, we'll build a RAG system that self-corrects inaccurate retrievals using: - for scraping - Milvus as vectorDB - Beam for deployment - Comet Opik for observability - LlamaIndex 🦙 for orchestration Let's go!

Avi Chawla

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32,011 views • 11 months ago •via X (Twitter)

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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 LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data.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:

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297,131 views • 2 years ago

Here is a 50-minute tutorial for those who want to build their first RAG application using open-source models. This is very beginner-friendly. As long as you are comfortable with Python, you should be able to get a ton from this.

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Big news, Claude just got a major upgrade today in Shipper, and we're very honoured to present it. From today on, Claude Opus 4.6 can build full-stack apps from natural language. We just launched a full-stack app builder for Claude to: → Turn ideas into apps: website, web/mobile app, extension etc → Code, design, launch, monetize → Do email marketing → Automatically translate its work → Self-maintain the apps it builds Claude's most powerful models can do all the above from a <10 word prompt, for as low as $0.28/app... And it takes minutes! Simply go to shipper, then ask Claude to "create a language-learning app" or "build a SaaS that charges $29/mo"! To celebrate the launch, we're giving away free credits randomly. Repost and comment "shipper" and we'll pick the winners.

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