You get:
- paying for tokens you don’t need (every API call adds up)
- slower responses from longer prompts
- context window filled with fluff instead of useful information
- prompts that are hard to maintain because they’re too long
- no systematic way to shorten prompts without breaking them
But compression opportunities exist:
- redundant phrasing: “please kindly summarize” → “summarize”
- verbose examples: 3 similar examples → 1 representative example
- unnecessary framing: “I want you to act as…” can often be shortened
- implicit instructions: things the model already knows
- whitespace and formatting: multiple line breaks, indentation
Without compression, you waste tokens and speed.
This prompt compresses prompts while preserving effectiveness.
Assume the role of a prompt optimization engineer who compresses prompts.
Your task is to reduce prompt length while maintaining or improving performance.
Generate:
1. ORIGINAL PROMPT STATISTICS
- Character count
- Token count (estimate)
- Number of examples
2. COMPRESSION OPPORTUNITIES IDENTIFIED
- Redundant phrases: [list]
- Verbose examples that can be condensed: [list]
- Unnecessary framing: [list]
- Implicit instructions (things model already knows): [list]
3. COMPRESSED PROMPT
- Full compressed version
4. SIDE-BY-SIDE COMPARISON
- Original vs. Compressed (with deletions shown, e.g., strikethrough)
5. PRESERVATION CHECK
- Does the compressed prompt still:
* Give the same instructions? (Yes/No — if no, explain)
* Handle edge cases? (Yes/No — if no, what was lost)
* Have the same output format? (Yes/No)
6. COMPRESSION RESULTS
- Original token count: [X]
- Compressed token count: [Y]
- Tokens saved: [Z] ([percentage]%)
- Estimated cost savings per 1K calls: [$]
7. VALIDATION PROTOCOL
- How to test that compression didn't break performance
INPUTS:
Original prompt:
[PASTE THE PROMPT]
Performance constraints:
[E.G., "Must maintain >95% accuracy on test set"]
Target token reduction:
[E.G., "At least 30%" — or "As much as possible without breaking"]
Model:
[GPT-4 / CLAUDE / GEMINI]
Critical use case (what can't break):
[E.G., "Safety classification — false negatives unacceptable"]
RULES:
- Don't remove instructions that handle edge cases or safety constraints
- Test compressed prompt before deploying (can be shorter but broken)
- Prioritize removing redundant instructions over condensing examples
- Flag if compression would require removing a necessary example
- Note that some prompts are already optimal (compression not always possible)
- Run this on any prompt that will be used at scale — token savings add up fast.
- Test the compressed prompt on your full test set before deploying.
- Pay attention to “preservation check” — if the compressed prompt loses important instructions, don’t use it.
- For critical use cases, be conservative — safety over token savings.
- Run compression regularly as prompts evolve — they tend to grow over time.
Original prompt:
“Hello, I would like you to please act as a helpful customer service assistant. Your job is to read the following customer message and then determine if the customer is angry, neutral, or happy. Please only respond with one word: ANGRY, NEUTRAL, or HAPPY. Do not add any other text. Do not explain your reasoning. Just the one word. Here is the customer message: {{message}}”
Performance constraints:
“Must maintain same accuracy on sentiment classification”
Target token reduction:
“As much as possible without breaking”
Model:
“GPT-4”
This framework improves outcomes by forcing:
- compression opportunity identification (find the waste)
- compressed prompt generation (shorter, same meaning)
- preservation check (did we break anything?)
- token savings calculation (quantified benefit)
- validation protocol (how to test before deploy)
Great prompt compression doesn’t sacrifice quality — it removes waste while preserving everything that matters.
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