Learn how to address the data freshness problem in Large Language Models (LLMs) through retrieval augmentation using LangChain and Pinecone vector database. Explore techniques to retrieve relevant information from external knowledge bases, enabling LLMs to access up-to-date information beyond their training data. Discover the process of data preprocessing, creating embeddings with OpenAI's Ada 002, setting up a Pinecone vector database, indexing data, and implementing generative question-answering with LangChain. Gain insights into adding citations to generated answers and understand the importance of retrieval augmentation in enhancing LLM performance.