caching strategies

**Caching strategies** involve storing the results of expensive computations or data retrievals so that subsequent identical requests can be served **faster and cheaper** without recomputing. In AI systems, caching is especially valuable because LLM inference is computationally expensive. **Types of Caching in AI Applications** - **Response Caching**: Store complete model responses for identical prompts. If the same question is asked again, return the cached answer instantly. - **Semantic Caching**: Cache responses based on **semantic similarity** rather than exact match. If a new query is semantically similar to a cached query (using embeddings), return the cached response. - **Embedding Caching**: Store computed embeddings for documents or queries to avoid recomputing them. - **KV Cache**: GPU-level caching of attention key-value pairs within the transformer during inference to avoid recomputing previous tokens. - **RAG Result Caching**: Cache retrieved document chunks for common queries to avoid repeated vector database lookups. **Cache Strategies** - **Write-Through**: Write to cache and storage simultaneously — ensures consistency but adds write latency. - **Write-Behind (Write-Back)**: Write to cache first, update storage asynchronously — faster writes but risk of data loss. - **Read-Through**: On cache miss, automatically load from storage into cache — simplifies application code. - **Cache-Aside (Lazy Loading)**: Application checks cache first; on miss, fetches from source and populates cache — most common pattern. **When to Cache** - **Deterministic Responses**: Cache when inputs reliably produce the same output (temperature=0, factual queries). - **Expensive Computations**: Cache when the cost of recomputation is high (LLM inference, large embeddings, complex aggregations). - **Frequent Requests**: Cache responses for commonly asked questions or popular queries. **Cache Invalidation** - **Time-Based (TTL)**: Entries expire after a fixed time period. - **Event-Based**: Invalidate when underlying data changes. - **Manual**: Explicitly clear cache entries when content is updated. **Tools**: **Redis**, **Memcached**, **GPTCache** (semantic caching for LLMs), **LangChain caching** (built-in response caching). Strategic caching can reduce LLM API costs by **30–80%** in production applications with repetitive query patterns.