How to cut LLM token costs & speed up AI apps

Large Language Model (LLM) token optimization is crucial for reducing API costs and improving the speed of AI applications by minimizing the consumption of tokens, which are the fundamental units of LLM interactions. Tokens can significantly impact both the cost and latency of AI apps, as input tokens are processed quickly in parallel, while output tokens are generated more slowly in sequence. This makes output token optimization particularly important. High costs can arise from verbose prompts, inefficient conversation histories, excessive output generation, and oversized Retrieval-Augmented Generation (RAG) contexts. Techniques such as prompt tightening, setting maximum token limits, semantic chunking, and caching can help in reducing token waste. Semantic caching, in particular, can yield substantial savings by storing and retrieving query vector embeddings and LLM responses for semantically similar queries, effectively bypassing redundant API calls. Redis offers a platform that integrates semantic caching, vector search, and session management, providing a streamlined approach to optimize token usage and improve app performance without complex infrastructure changes.