---
title: "Prompt Caching: Structure Requests for Reuse"
description: "Place stable prefixes first, separate volatile data, monitor cache hits, and protect tenant boundaries."
language: en
canonical: https://navinresearch.com/blog/inference/prompt-caching.md
topics: ["prompt caching", "LLM context cache", "AI inference cost"]
---

# Prompt Caching: Structure Requests for Reuse

Place stable prefixes first, separate volatile data, monitor cache hits, and protect tenant boundaries.

**Short answer:** Caching works best when many requests share an identical or sufficiently similar prefix. Put stable instructions and reusable reference material before volatile user data, avoid meaningless prompt changes, and verify provider-specific caching, retention, and billing behavior.

## Implementation steps

1. Measure repeated input volume by prompt segment.
2. Order content from stable to dynamic where the API recommends it.
3. Version shared prompts intentionally instead of injecting timestamps.
4. Monitor cached-token usage, latency, cost, and correctness.

## Validation checklist

- Test cache isolation across tenants.
- Confirm policy updates invalidate old prefixes.
- Compare total cost at production request rates.

## Common mistakes

- Caching user-specific secrets in shared layers.
- Assuming every request receives a cache hit.
- Changing stable prompts on every deployment without need.

## Practical decision

Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.

## Official sources

- [Google, Gemini API context caching](https://ai.google.dev/gemini-api/docs/caching)
- [OpenAI, Latency optimization guide](https://platform.openai.com/docs/guides/latency-optimization)

---

Reviewed: 2026-08-24. This concise engineering note is educational, not a claim that Navin Research created or independently verified the cited provider technology.