How to Reduce Token Usage

Reducing token usage can lower AI API costs, improve response times, and allow more useful information to fit inside a model's context window.

What consumes the limit

Start by removing redundant instructions. Prompts often grow over time because developers repeatedly add rules without deleting older ones. If three instructions communicate the same requirement, consolidate them into one clear statement.

Next, control conversation history. A chatbot does not always need every message from a six-month conversation. Older information can be summarized while recent messages remain available verbatim.

Leave room for a useful response

Retrieval is another important technique. Instead of passing an entire documentation library into every request, search it first and provide only sections related to the current question.

Also examine model responses.

Check the model-specific constraint

If users only need a three-sentence answer, explicitly limit the output instead of allowing the model to generate several pages. Output tokens can be considerably more expensive than input tokens for many models.

Additional techniques include:

Practical checks before you proceed

Use concise system prompts Remove duplicate examples Store state outside the conversation Cache repeated prompt prefixes when supported Use structured data efficiently Avoid unnecessary HTML or metadata Trim retrieved documents Select smaller models for simple work

Do not optimize blindly, however.

Practical checks before you proceed

A 20% shorter prompt is not an improvement if it causes twice as many failed responses.

Measure tokens per successful task rather than tokens per request. The goal is to remove unnecessary context while preserving the instructions and information that the model genuinely needs.