Lizard inference engineering: Prompt caching should follow conversation structure
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Inference Engineering · Day 21 · Evening
The same cache can be a product feature and a benchmark contaminant.
A conversation naturally repeats its previous prefix, so retaining prompt state can avoid reprocessing the whole transcript. A benchmark that repeats an identical prompt for measurement should not accidentally turn that reuse into a fake prefill win.
Cache policy should follow workload semantics, not simply remain globally enabled.
Which caches in your stack need different rules for production and benchmarking?
Engineering fact: Lizard Chat can reuse prior prompt state for the same conversation while benchmark paths avoid chat-only cache behavior that would make repeated prompts look artificially cheap.
#LizardNative #LizardLLM #LocalAI #GGUF #InferenceEngineering
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