Prompt Caching & Memoization

Expert

Cache repeated prompt-response pairs to eliminate redundant API calls.

01 The Concept

Many production systems send identical or near-identical prompts repeatedly. Prompt caching stores response results keyed by prompt hashes, serving cached responses for repeated queries. This dramatically reduces API costs and latency for common queries.

02 Weak vs. Strong

EX 01Prompt Caching Pipeline

prompt_hash = hash(system_prompt + user_query) if cache.exists(prompt_hash): return cache.get(prompt_hash) # Instant, free response = call_llm(system_prompt, user_query) cache.set(prompt_hash, response, ttl=3600) # Cache for 1 hour return response

→ Why it works

For FAQ-heavy applications, caching can eliminate 60-80% of API calls, cutting costs dramatically.

03 Key Points

01Hash-based caching: Computing deterministic hashes of prompts for cache lookup.

02TTL policies: Setting time-to-live expiration for cached responses.

03Semantic caching: Matching semantically similar (not just identical) queries to cached results.

04Cache invalidation: Clearing caches when prompts or models are updated.

05Cost reduction: Eliminating API calls for frequent, repetitive queries.

04 Model-Specific Notes

Claude handles Prompt Caching & Memoization tasks with excellent instruction compliance and structured output formatting.

05 For Your Role

Think of Prompt Caching & Memoization as organizing your work systematically so every step is clear and repeatable.

Next: Latency-Optimized Streaming →