This video goes beyond just being comprehensive and beginner-friendly delivering a wealth of valuable information concisely. It effectively ties together concepts covered in courses here.. The creator emphasizes the importance of understanding the basics to build upon those concepts and effectively troubleshoot complex systems. He systematically guides viewers through fundamental concepts like large language models, Lang chains, chains, agents, and RAG systems. Including practical examples and visual demonstrations, particularly the Airbnb chatbot and the workflow for uploading files to Pinecone, makes it easier to grasp these concepts. The discussion extends beyond just theory to practical considerations for building high-quality AI solutions, such as: - Data quality - Prompt engineering, with a detailed framework provided - Integration with other applications using APIs and webhooks The video also highlights the future potential of AI agents, especially multi-agentic systems, and encourages viewers to act now to capitalize on the emerging market.

After two years of building with GenAI, here’s what I wish I’d had from day one... This has been a long time in the making, and I’m excited to finally pull back the curtain on what we’ve been building behind the scenes: The GenAI Launchpad — officially launched on Product Hunt today! 🎉 For the past two years, my team and I at Datalumina have been deeply involved in the world of AI, building solutions with large language models (LLMs) for clients across industries. Each project taught us so much about what it takes to bring AI to life in practical, high-impact ways. But there was one recurring challenge... We spent way too much time setting up project structures, handling integrations, and putting out fires in the infrastructure — leaving less time for the real AI work, the work that brings ideas to life. Not only was setup eating into our time, but we also found that the agent frameworks on the market were just too optimistic. Real-world use cases are more complex and demand reliability and precision that many frameworks simply can’t deliver. So, we got to work! 👷🏼‍♂️ And after two years of trial and error, working with every system and structure you can imagine, we built our own solution. The GenAI Launchpad is the result of our journey — a project repository that streamlines everything from initial setup to deployment, ready to handle the demands of production at scale. And the time savings? ⏳ We’ve calculated that it saves us over 50 hours per project on average, so we can dive right into the creative work that actually advances AI. Today, we’re launching the GenAI Launchpad to share that time-saving power with you — our community of fellow AI enthusiasts and builders. This is more than just a repository; it’s a battle-tested, engineer-approved blueprint that I wish I’d had when we started. It’s here to help you skip the headaches, bypass the boilerplate, and focus on what matters: building innovative AI solutions for real-world problems. If you’ve ever spent weeks fighting project setup, only to finally reach the real work, then you’ll understand why I’m so excited to share this.

The goal of this group is to help you navigate the complex and rapidly evolving world of data science and artificial intelligence. This is your hub to stay up-to-date on the latest trends, learn specialized skills to turn raw data into valuable insights, connect with a community of like-minded individuals, and ultimately, become a Data Alchemist. Together, let's decode the language of data and shape a future where knowledge and community illuminate our way. Rules - Don't sell anything here or use Data Alchemy as any kind of funnel - We delete low effort community posts, and posts with poor English. Proofread your post first. - Help us make the posts high quality. If you see a low quality post, then click on the 3 dots on the post and "Report To Admins". Start by checking out these links - Classroom - Introduction - Roadmap - Contribution Be Aware of Scammers - Please be aware that this is a public group. Unfortunately, some people abuse the Skool platform to send DMs or post comments to trick people. This is the internet, so always do your own due diligence. Never automatically trust someone here on the Skool platform other than @Dave Ebbelaar's official account. To kick things off, please comment below, introducing yourself. Let us know: 1. Your name and where you're from 2. What project(s) you're currently focused on See you in the comments!

When building RAG systems, embeddings + text search is better than embeddings on their own Combining these techniques together is called Hybrid Search. In this tutorial, I walk you through building a hybrid search pipeline using just PostgreSQL. On top of that, we'll also look at how we can further improve the accuracy using Reranking. Curious how a hybrid system works and how it can optimize your search accuracy for AI applications? Watch the video here P.S. I was supposed to record this from my new office, but, well… life had other plans. So instead, enjoy this kitchen table special — different setup, same Dave vibes. I think it turned out pretty alright!

If you’re a developer, you’ve probably seen Cursor IDE making waves recently. It’s been everywhere, and after five years with VS Code, I made the switch — for now. Cursor is a fork of VS Code but adds powerful AI features like autocomplete, inline edits, and a composer. If you’re familiar with VS Code, the transition is seamless. So you can still use all your settings, themes, extensions, and most importantly, the interactive window! In this week’s video, I dive into these AI features, show how they can improve your coding workflow, and help you decide if Cursor is right for you. Talk soon, Dave P.S. We’ve temporarily closed enrollment for Data Freelancer as we’ve hit our capacity. But don’t worry — some exciting updates are coming soon. We're working on some big things behind the scenes for both Datalumina as a company and the Data Freelancer program. Stay tuned for more details!