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.