Prompt Caching and Tool Design: Moving Long System Prompts out of the Cost Center

In 2026, mainstream LLM APIs universally support prompt caching or equivalent semantic features, offering substantial discounts for requests with a stable prefix and dynamic suffix. To leverage this discount, the key is keeping a stable byte sequence rather than writing long prose in your system prompt.

1. Structuring for Cache Hits

1. Place persona, guidelines, and output schemas inside the static prefix, and user-dependent dynamic variables in the suffix.
2. Avoid injecting volatile components (such as timestamps, random IDs, or shuffled few-shot examples) into the prefix.
3. Use a deterministic JSON Schema for tool definitions to lock key order (if your generator client shuffles keys, the cache key becomes invalid).

2. Tool Design Best Practices

• Granularity: Prefer several single-purpose, "narrow" tools over one "all-in-one" JSON blob that forces the model to guess parameters.
• Error Handling: Return machine-readable error_code outputs so the LLM can self-correct, and you can aggregate errors easily in your tracing system.

Recommended reading alongside our 2023 guide "10 ChatGPT Prompting Techniques": that post covers how to write effective prompts, while this one focuses on keeping long-context and multi-turn runs cost-effective and deterministic.