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Video: Build a RAG-Powered Chatbot in Five Minutes

Retrieval-augmented generation (RAG) is exploding in popularity as a technique for boosting large language model (LLM) application performance. From highly…

Retrieval-augmented generation (RAG) is exploding in popularity as a technique for boosting large language model (LLM) application performance. From highly accurate question-answering AI chatbots to code-generation copilots, organizations across industries are exploring how RAG can help optimize processes.

According to State of AI in Financial Services: 2024 Trends, 55% of survey respondents reported they were actively seeking generative AI workflows for their companies. Customer experience and engagement were the most sought-after use cases, with a 34% response rate. This suggests that financial services institutions are exploring chatbots, virtual assistants, and recommendation systems to enhance the customer experience.

In this five-minute video tutorial, Rohan Rao, senior solutions architect at NVIDIA, demonstrates how to develop and deploy an LLM-powered AI chatbot with just 100 lines of Python code—and without needing your own GPU infrastructure.

imgJoin us in person or virtually for retrieval-augmented generation (RAG) sessions at NVIDIA GTC 2024.

Key takeaways

Summary

Start with a foundation model to quickly begin LLM experimentation. With NVIDIA AI Foundation Endpoints, all embedding and generation tasks are handled seamlessly, removing the need for dedicated GPUs. Check out these resources to learn more about how to augment your LLM applications with RAG: 

Source:: NVIDIA

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