How to get started with Gemini Flash
What is Gemini Flash?
Imagine a large language model (LLM) that's lightweight, super-fast, and cost-effective. That's exactly what Gemini Flash brings to the table. It boasts impressive features like:
Multimodal reasoning: Can handle text, audio, and even code!
Massive context window: Up to 1 million tokens, allowing you to process massive amounts of data (think hours of audio or thousands of lines of code).
Optimized for performance: Delivers high-quality results at a lower cost, perfect for enterprise use.
Your Beginner's Guide to Getting Started with Generative AI
Over the past couple of years, I've had the privilege of building and launching Gemini Code Assist and Gemini for Google Cloud alongside Google’s talented product and engineering teams. Teaching is a passion of mine, and I've received countless requests to break down the fundamentals of Gen AI. So, I'm thrilled to share that I just put together a videos series "10 Days of Gen AI".
Whether you're a seasoned developer or just dipping your toes into the world of AI, this series will equip you with the knowledge and tools you need to harness the power of Gen AI. Let's embark on this exciting journey together!
The Secret Sauce of RAG: Vector Search and Embeddings
Retrieval-Augmented Generation (RAG) leverages the strengths of Large Language Models (LLMs) and external knowledge bases to deliver more informative and accurate outputs. Here's a breakdown of the key components focusing on data chunking, embeddings, vector databases, and their interaction
How to Make Your Generative AI More Factual
Large language models are powerful tools, but ensuring their accuracy is essential. Retrieval-Augmented Generation (RAG) emerges as a game-changer, bridging the gap between raw LLM potential and reliable, factual outputs. By harnessing the power of external knowledge bases, RAG empowers LLMs to deliver more informative, contextually relevant, and up-to-date responses across various industries. From personalized e-commerce experiences to enhanced medical diagnosis assistance, the applications of RAG are vast and hold immense promise for the future of Generative AI.
