Which component of the RAG architecture is responsible for adding external data to the RAG process?
この演習はコースの一部です
理論を実践に変える、インタラクティブな演習のひとつをお試しください
In this module, you learn about generative AI applications, and types of applications that can use the power of generative AI. You also learn about foundation models provided by Google, and challenges that exist when using generative AI in your applications.
In this module, you learn about generative AI prompts. A prompt is a natural language request submied to a language model to request a response back. You can design your prompts to improve the results being returned from a model.
In this module, you experiment with Vertex AI Studio, which provides tools to rapidly prototype, tune models with your own data, and seamlessly deploy to applications. You explore multimodal capabilities of Gemini, design prompts, and generate conversations.
In this module, you learn how to improve the accuracy of foundation models. You learn about retrieval augmented generation, or RAG, a technique for grounding a foundation model with external sources of knowledge. You'll also see an example RAG-capable generative AI solution architecture on Google Cloud.
現在の演習
In this module, you build a chat application that uses large language models (LLMs) and retrieval augmented generation (RAG) to create engaging and informative conversations.
Link to lesson PDFs
