What is Prompt Caching? Optimize LLM Latency with AI Transformers

2026-02-07 · Source: IBM Technology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

Prompt caching is a technique to optimize large language model (LLM) performance by storing precomputed key-value (KV) pairs generated during the "prefill phase" of processing an input prompt. Unlike output caching, which stores the LLM's final response, prompt caching focuses on the input itself, specifically the internal representations (KV pairs) of tokens within the prompt. This method is particularly effective for lengthy, static prompt components like 50-page documents, system instructions, few-shot examples, tool definitions, or conversation history. By caching these KV pairs, subsequent requests that share the same prompt prefix can skip reprocessing the static content, significantly reducing latency and computational cost. For optimal performance, prompts should be structured with static content placed at the beginning to facilitate prefix matching, and caching typically benefits prompts exceeding 1024 tokens due to overhead considerations. Cache entries are usually cleared after 5-10 minutes, though some persist longer.

Key takeaway

For AI Engineers and Machine Learning Engineers deploying LLMs, understanding and implementing prompt caching is crucial for optimizing operational costs and user experience. You should structure your prompts to place static elements like system instructions, large context documents, or few-shot examples at the beginning. This enables efficient prefix matching and reuse of precomputed KV pairs, directly reducing latency and API costs, especially for applications involving repeated queries against a consistent knowledge base.

Key insights

Prompt caching stores precomputed LLM input representations (KV pairs) to reduce latency and cost for repeated prompt prefixes.

Principles

• Cache KV pairs, not LLM outputs.
• Static content first optimizes prefix matching.
• Caching benefits prompts >1024 tokens.

Method

LLMs compute KV pairs for each token at every transformer layer during the prefill phase. Prompt caching stores these KV pairs for static input prefixes, allowing subsequent requests with matching prefixes to reuse them, avoiding recomputation.

In practice

• Cache system prompts and few-shot examples.
• Place large documents at prompt start.
• Use for multi-question interactions on same context.

Topics

• Prompt Caching
• Large Language Models
• Transformer Architecture
• LLM Latency Optimization
• Key-Value Pairs

Best for: AI Engineer, Machine Learning Engineer, Prompt Engineer

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• Prompt caching - Mistral AI — Prompt caching reuses shared prompt prefixes to reduce LLM inference costs and latency.
• Prompt Caching with the OpenAI API: A Full Hands-On Python Tutorial — Prompt caching reuses repeated LLM input prefixes to enhance API app speed and cost-efficiency.
• Shipping LLMs (Part 1/6): Prompt Caching vs Semantic Caching — Prompt caching is lossless and exact, while semantic caching is lossy and similarity-based, impacting correctness.
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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.