5 Proven Techniques for Token Compression and Prompt Optimization

Every token counts. Whether you're building production applications with large language models (LLMs) or running experiments in a notebook, bloated prompts silently drain budgets and degrade response quality. Token compression is the practice of transmitting more intent with fewer tokens, and prompt optimization is how you structure that intent so models respond accurately and efficiently. This guide covers five techniques you can apply right away to reduce token consumption without sacrificing output quality, along with the reasoning behind each approach and practical code examples.

1. Replacing Verbose Instructions with Structured Constraints

Long, conversational system prompts feel natural to write but cost significantly more than tightly structured equivalents. The fix is moving from narrative instructions to declarative constraints, using schema-like formatting that models parse efficiently. Instead of writing:

Please make sure that when you respond, you always use
bullet points and keep answers under 100 words. Do not
include any preamble or sign-off at the end of your reply.

Compress it to:

Format: bullet points | Max: 100 words | Omit: preamble, sign-off

That single line replaces 36 tokens with roughly 14. Across thousands of API calls, the savings compound quickly. Use pipe-delimited key-value pairs, YAML-style constraints, or JSON schema snippets depending on the model family you're working with.

2. Using Few-Shot Examples Strategically, Not Exhaustively

Few-shot prompting — providing example input-output pairs before your actual request — dramatically improves output format consistency. The mistake most practitioners make is adding too many examples. Research from Anthropic and academic benchmarks consistently shows diminishing returns beyond three to five examples for most classification and generation tasks. Here's a lean three-shot prompt for sentiment labeling: