Caching Strategies for LLM Outputs

Key Concepts

• 1Caching LLM and retrieval outputs cuts cost and latency by reusing prior work, but commerce data's volatility makes naive caching dangerous
• 2Safe-to-cache vs unsafe: stable content (product descriptions, general answers) caches well; volatile facts (price, stock, personalized results) risk serving stale, wrong data
• 3Prompt/prefix caching reuses the cost of a shared prompt prefix across calls, a cheap win that doesn't risk staleness because only the model's compute is reused
• 4Semantic caching (returning a cached answer for a similar-enough query) saves the most but risks subtle mismatches, so it needs careful similarity thresholds and guardrails
• 5Cache invalidation is the hard problem: price and inventory changes must evict affected cached results, or the agent confidently serves outdated information
• 6Personalization limits caching: per-user results are less cacheable, so caching often applies to shared, non-personalized layers (catalog facts, common queries)
• 7The PM tradeoff: aggressive caching maximizes savings but raises staleness risk; the right policy is per-data-type, matching cache TTL to how fast the data changes

Why It Matters for PMs

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