Many companies are seeing their bills for large language model (LLM) application programming interfaces (APIs) explode, driven by users asking the same questions in different ways, according to Sreenivasa Reddy Hulebeedu Reddy, an AI application developer. Reddy found that users frequently rephrased the same questions, causing redundant calls to the LLM and incurring unnecessary API costs.

Reddy's analysis of query logs revealed that users were asking questions like "What's your return policy?", "How do I return something?", and "Can I get a refund?" separately, each generating nearly identical responses and incurring full API costs. Traditional, exact-match caching, which uses the query text as the cache key, proved ineffective, capturing only 18% of these redundant calls. "The same semantic question, phrased differently, bypassed the cache entirely," Reddy explained.

To address this, Reddy implemented semantic caching, a technique that focuses on the meaning of queries rather than their exact wording. Semantic caching analyzes the underlying intent of a question and retrieves the answer from the cache if a semantically similar query has already been processed. After implementing semantic caching, Reddy reported a cache hit rate increase to 67%, resulting in a 73% reduction in LLM API costs.

The core challenge with traditional caching lies in its reliance on exact matches. As Reddy illustrated, traditional caching uses a hash of the query text as the cache key. If the key exists in the cache, the cached response is returned; otherwise, the query is processed by the LLM. This approach fails when users phrase questions differently, even if the underlying meaning is the same.

Semantic caching represents a significant advancement in optimizing LLM API usage. By understanding the semantic meaning of queries, it can drastically reduce redundant calls and lower costs. However, implementing semantic caching effectively requires careful consideration of various factors, including the choice of semantic similarity algorithms and the management of cache invalidation. The development highlights the importance of moving beyond simple, text-based caching solutions to more sophisticated methods that understand the nuances of human language.