Semantic caching is an advanced retrieval optimization method that enhances AI application performance by storing and reusing previously retrieved results based on the semantic meaning of queries rather than exact matches. Unlike traditional caching, which relies on syntactic representation, semantic caching evaluates the meaning and context of data to provide efficient responses to similar queries, as exemplified by the difference between exact and semantically equivalent questions about the capital of Brazil. This approach is particularly beneficial in Retrieval-Augmented Generation (RAG) applications, where it reduces computational load and costs by eliminating repetitive searches and response generation, especially when using expensive language model APIs. Semantic caching is well-suited for question-answering systems, as it efficiently handles consistent queries by storing questions and their corresponding answers in a key-value format, but it is less ideal for applications requiring diverse responses. The implementation of semantic caching, as demonstrated by the use of Qdrant, enables AI systems to retrieve answers more swiftly and accurately, improving scalability and performance in data retrieval tasks while offering potential cost savings.