Exact, semantic and prefix caching in front of LLMs: scoped keys, distances with an error budget, tenant isolation, invalidation and measured savings.
Engineers designing AI products and the systems that serve them.
Your approach: Explain the AI behavior and system boundaries in the brief, with evidence for the requested quality, privacy, latency and cost.
Design a Prompt and Semantic Cache for LLMs. Exact, semantic and prefix caching in front of LLMs: scoped keys, distances with an error budget, tenant isolation, invalidation and measured savings. Work from the scoping questions below. State assumptions for any unspecified load, guarantee or target, then trace your design end to end. Explain one difficult case and a credible alternative; the worked example is a reference, not a required implementation.
Resolve the scoping questions for a Prompt and Semantic Cache for LLMs. Separate stated behavior from assumptions, and identify what is outside your design.
Declare relevant volume, latency, freshness, quality or cost targets with units. Show calculations or an evaluation plan that can test them; unspecified targets are your assumptions, not hidden pass criteria.
Explain how your guarantees hold in a difficult case relevant to this subject. Address: Multi-turn chat? Self-hosted models?
Identify users, required behavior and exclusions. Answer: Who calls it? How much?
Define the information owned by the system and the inputs, outputs and errors at its boundaries. Resolve: What may be shared? How wrong may a hit be?
Estimate the dominant workload and resource demand with units and explicit assumptions. For a learned system, also state how quality is measured and what data is available.
Draw or describe the responsibilities needed for a Prompt and Semantic Cache for LLMs. Trace a representative request, event or job from its input to a visible result; identify durable state owners.
Walk through a difficult case step by step, including detection and recovery. Consider: How fresh? Latency budget? Multi-turn chat? Self-hosted models?
Compare a credible alternative using your chosen workload and guarantees. Explain a remaining risk, a signal to watch and when you would change the design.