Tutorials on Rag

Hello and welcome on Part 3 of our tutorial on how to build sophisticated full stack applications with Bolt , UX Pilot , Supabase , and ChatGPT . In Part 1 of our tutorial, we set the stage and created a plan for our app, including our Phase 1 (aka detailed MVP ) features, a UI Development Plan , and a Design System we can use to create a beautiful app of this kind. In Part 2 , we took this plan, put it to good use, and created an actual High Fidelity design for each and every part and each and every screen of our app.

Today we often hear about incredible AI advancements that promise to make our lives easier. But besides developing and improving new AI models, we also find new ways to use them and utilize their full potential. One exciting feature of LLMs AI Retrieval-Augmented Generation, or RAG for short. This system connects real time data to the power of AI models. And knowing how RAG works really raises the ceiling of your expertise as an AI engineer. So, in this opening article let's make sure to cover all the core fundamental concepts. And in the upcoming articles we will build exciting applications to apply our knowledge in practice. Large language models (LLMs) generate text by predicting the most probable next word, but without access to real-time or domain-specific information, they produce errors, outdated answers, and hallucinations.