Building your own RAG application using Together AI and LlamaIndex

You can build a powerful Retrieval Augmented Generation (RAG) application using Together AI's cloud platform and LlamaIndex, which provides fast and cost-efficient training without requiring technical expertise to train a model. This approach leverages both generative models and retrieval models to improve knowledge-intensive tasks by providing up-to-date information from external data sources during response generation. By creating a vector store and indexing source documents using an embedding model of your choice, you can retrieve relevant information, augment it with the original query, and use a large language model (LLM) to generate accurate responses. This approach has been demonstrated through a quickstart example that incorporates a new article into a RAG application using the Together API and LlamaIndex. The tools provide numerous advantages, including faster training, lower costs, and improved performance, making it an attractive option for building innovative solutions.

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