🚀 Introducing the ultimate RAG Cheatsheet! 💣
Thrilled to share this masterpiece with you all. Capturing every detail of RAG is a challenging task—it's practically a whole new world within Generative AI. But, I believe I've nailed down most of what you need to know for its implementation.
Reflecting on my early days exploring Generative AI, I recall my trip to Munich last July while trying to demystify RAG. Initially, it felt straightforward: RAG is almost like using the book during the exam when you don't know the answer. Yet, as often is the case with shortcuts, there's a spectrum of techniques involved. That realization pushed me to dive deeper and truly immerse myself in it.
Over the recent weeks, I've been busy pulling together and making sense of all the content I've dived into, all to make it ready-to-eat for you. What we've got now is this super visual cheatsheet, laid out across the following key areas:
💡 RAG Foundations
💡Chunking Strategies
💡Frameworks & Vector DBs: The Milvus Project Chroma Vespa.ai Qdrant Zilliz Epsilla (YC S23) Weaviate Pinecone LlamaIndex LangChain
💡Advance Retrieval Techniques
💡Generation
💡RAG Evaluation & Frameworks
And there's more! I also want to highlight some incredible people who've made my learning journey much smoother and educational: Andre Zayarni (Qdrant Co-Founder), Cobus Greyling (great posts at Medium), Elvis S. (Top ML Papers), Aurimas Griciūnas (diagrams made simple), Daniel Svonava (Superlinked Co-Founder), Pavan Belagatti (great RAG examples), Anthony Alcaraz (everyday one new post in Medium), Leonie Monigatti (Developer advocate at Weaviate), Bob van Luijt (Weaviate Co-Founder), Jerry Liu (LlamaIndex Co-Founder), Tom Yeh (Colorado Boulder University)
Don't miss out—grab the cheatsheet now! It's ready for download at my Miro space right here in the comments 👇
#rag #genai #llmops #ai