An Engineering Guide to LLM Token and Cost Optimization

Description and use

<p>For application and platform engineers, and for teams using AI coding tools.</p> <p><strong>Saving tokens is not mainly about shortening prompts. It is about making the model do less useless work.</strong> Repeated reads, irrelevant context, verbose output, failed attempts, and rework all belong in the optimization budget.</p> <p>The goal is to reduce total cost per accepted task while meeting quality, safety, and latency requirements. This guide focuses on usage-priced model APIs; subscriptions and IDE plans need their own quota analysis.</p> <h2> How to use this guide </h2> <p>This is not

Problem

<p>For application and platform engineers, and for teams using AI coding tools.</p> <p><strong>Saving tokens is not mainly about shortening prompts. It is about making the model do less useless work.</strong> Repeated reads, irrelevant context, verbose output, failed attempts, and rework all belong in the optimization budget.</p> <p>The goal is to reduce total cost per accepted task while meeting quality, safety, and latency requirements. This guide focuses on usage-priced model APIs; subscriptions and IDE plans need their own quota analysis.</p> <h2> How to use this guide </h2> <p>This is not

Outcome

<p>For application and platform engineers, and for teams using AI coding tools.</p> <p><strong>Saving tokens is not mainly about shortening prompts. It is about making the model do less useless work.</strong> Repeated reads, irrelevant context, verbose output, failed attempts, and rework all belong in the optimization budget.</p> <p>The goal is to reduce total cost per accepted task while meeting quality, safety, and latency requirements. This guide focuses on usage-priced model APIs; subscriptions and IDE plans need their own quota analysis.</p> <h2> How to use this guide </h2> <p>This is not

Evidence

An Engineering Guide to LLM Token and Cost Optimization

Source