Minimize Token Usage

Token usage is the amount of text your assistant has to read and write. The more text it reads or writes, the more AI credits you may use.

This guide is written for non-technical Ampere users. You do not need to understand models, APIs, or billing internals to use it.

The Simple Rule

Ampere spends more credits when your assistant has to:

• read long chats
• read big files
• read repeated instructions
• read large tool results
• write very long answers
• use a powerful model for a simple task

To reduce usage, give the assistant less noise and ask for shorter, clearer answers.

How Ampere Usage Works

Ampere credits are easier to understand than raw AI provider billing:

$1 = 1,000 Ampere credits.

For normal chat, credits mainly depend on two things:

• What the assistant reads — your message, previous chat context, files, memory, tool results, and instructions
• What the assistant writes — the final answer, plan, code, summary, or report

Different models have different prices. A powerful model costs more than a smaller model, so model choice matters.

Best Prompts to Save Credits

Use these patterns when you want lower usage.

Ask for a short answer

Give me the answer in 5 bullets. Do not repeat the background.

Give me only the next action and the reason.

Keep this under 150 words.

Ask for a focused answer

Only check the pricing section and tell me what is wrong.

Look at this error and suggest the most likely fix. Do not review the whole project.

Compare only these two options and recommend one.

Ask for a summary before continuing

Summarize this long conversation into decisions, current status, and next steps. Then continue from the summary.

This is useful when a task has gone on for many messages.

Ask for changes only

Tell me only what changed since the last version.

Show only the edited text, not the full document.

Give me the final copy only.

What Not To Do

Avoid these habits if you want to save credits.

Choosing the Right Model

Use a cheaper or balanced model for:

• short summaries
• formatting
• rewriting simple text
• extracting key points
• simple customer replies
• small brainstorms

Use a stronger model for:

• coding
• debugging
• legal or security-sensitive review
• architecture decisions
• complex research
• final business-critical copy

A good workflow is:

1. Start with a cheaper model for simple exploration.
2. Use a stronger model when the task becomes difficult.
3. Ask for a short summary before switching models.

Sharing Files Without Wasting Credits

When working with files, send the smallest useful part.

Better:

Check the introduction and pricing section only.

Better:

The bug is in the checkout flow. Inspect only the checkout files first.

Better:

Here are the 30 lines around the error.

Avoid:

Read the whole repo and find issues.

Here is a full 10,000-line log.

I pasted the full document again so you remember it.

Keeping Long Projects Cheap

Long projects can become expensive because every turn may bring old context forward.

Use this process:

1. Start with a short goal.
2. Share only the relevant context.
3. Ask for a small plan.
4. Work one step at a time.
5. After a few steps, ask for a compact summary.
6. Continue from the summary, not the full messy chat.

Useful prompt:

Before continuing, compress this task into: goal, decisions, files, blockers, and next action. Keep only what is needed.

Using Memory the Right Way

Memory helps save repeated explanation.

Good things to remember:

• your preferred answer style
• your company/product context
• stable project rules
• naming preferences
• repeated workflows
• important decisions

Examples:

Remember: for Ampere docs, keep writing simple and customer-facing.

Remember: when I ask for research, give practical business takeaways first.

Do not save temporary clutter. Memory should stay clean.

For Teams

If your team uses Ampere, agree on simple rules:

• default to short answers
• ask for summaries during long threads
• do not paste full logs unless needed
• save repeated company context in memory or docs
• use premium models only for premium work
• ask for “final answer only” when you do not need reasoning

This keeps usage lower and makes answers easier to read.

Quick Checklist

Before sending a message, ask yourself:

• Can I ask this in fewer words?
• Can I give only the relevant file section?
• Do I need a long answer, or just the next step?
• Is this simple enough for a cheaper model?
• Have I repeated instructions that should be saved once?
• Is this long chat ready for a summary?

Examples

Expensive prompt

Read everything from our old chat, all these logs, the full document, and give a detailed explanation of every possible issue.

Better prompt

Check only the checkout error below. Give me the top 2 likely causes and the next action.

Expensive prompt

Rewrite this whole page and explain every change.

Better prompt

Rewrite this page for non-technical customers. Return only the final copy.

Expensive prompt

Use the strongest model and deeply analyze this simple caption.

Better prompt

Make this caption clearer in 3 options. Keep each under 12 words.

What We Learned From Research

We reviewed current AI provider docs plus Reddit and X discussions about high AI-agent costs. The same advice appeared repeatedly:

• the biggest waste is usually too much unnecessary context
• long tool outputs and full logs can drain usage quickly
• smaller models should handle simple tasks
• stronger models should be saved for hard decisions
• long conversations should be summarized before continuing
• focused workflows beat “read everything” workflows
• shorter answers often improve both cost and clarity

The practical takeaway for Ampere users is simple: give the assistant the right context, not all context.