Learn how to use Llama Index (formerly GPT Index) to store documents in vector store indexes and query them effectively. Explore techniques for adding multiple documents to the same index, creating multiple indexes using both 'from documents' functionality and nodes, and customizing the language model and prompt helper for setting token limits. Gain hands-on experience with creating, loading, and querying indexes, creating indexes from nodes, reusing nodes across index structures, adding documents to existing indexes, and defining language models and prompt helpers. Perfect for developers looking to enhance their skills in working with large language models and vector stores for document retrieval and question-answering systems.