This article discusses the challenges and solutions in building applications using large language models (LLMs) such as OpenAI's ChatGPT. The three main challenges are high costs, lack of up-to-date information, and need for domain-specific knowledge. Two proposed frameworks to address these issues are fine-tuning and caching + injection. LlamaIndex is a powerful tool that can abstract much of the latter framework. The article introduces LlamaIndex as a "black box around your Data and an LLM" and explains its four main indexing patterns: list, vector store, tree, and keyword indices. It then demonstrates how to create and save a persistent vector index using LlamaIndex with both local and cloud vector databases (Milvus Lite and Zilliz). In summary, the article provides an overview of LlamaIndex, its applications in LLM-based applications, and offers guidance on creating and managing persistent vector store indices for real-world use cases.