Prompt Compression

Prompt compression is the skill of reducing prompt length while preserving intent, constraints, and output quality.

In real systems, you cannot afford long prompts everywhere.

Token limits, latency, and cost force engineers to write prompts that are compact but precise.

Why Prompt Compression Matters

Every extra token:

• Costs money
• Increases latency
• Consumes context window

In production, long prompts quickly become a bottleneck.

Prompt compression is what separates demos from deployable systems.

The Core Goal of Compression

Compression does not mean removing clarity.

The goal is to:

• Remove redundancy
• Replace verbose language with structure
• Encode intent efficiently

You are optimizing information density.

Baseline: An Uncompressed Prompt

Let’s start with a realistic but inefficient prompt.

You are an expert software engineer.
Your task is to review the following code carefully.
Please identify any bugs, explain why they occur,
and suggest improvements in a clear and structured way.
Do not include unnecessary commentary.
Focus only on logic, performance, and readability.
  

This prompt works, but it wastes tokens.

Notice how the same intent is repeated in different wording.

First Compression Pass: Remove Redundancy

We remove repetition without changing meaning.

Act as an expert software engineer.
Review the code and identify bugs,
explain causes, and suggest improvements
focused on logic, performance, and readability.
  

Same intent. Fewer tokens. No loss in clarity.

Second Compression Pass: Structural Encoding

Now we encode instructions structurally instead of narratively.

Role: expert software engineer
Task:
- find bugs
- explain cause
- suggest improvements
Focus: logic, performance, readability
  

This version is shorter and more machine-aligned.

Models respond well to lists and labels because they reduce ambiguity.

Why This Works Internally

Internally, the model:

• Identifies role constraints
• Parses task bullets
• Applies focus filters

Narrative words are no longer needed for reasoning.

Compression Using Abbreviations

In advanced systems, abbreviations are used consistently.

Role: SWE
Task: bug find + cause + fix
Focus: logic | perf | readability
  

This works only if abbreviations are well understood in your system.

Compression trades readability for efficiency.

Prompt Compression vs Prompt Quality

Over-compression can break prompts.

Bad compression results in:

• Vague outputs
• Missing constraints
• Unexpected tone

Always test compressed prompts against the original.

Real-World Use Case: RAG Systems

In Retrieval-Augmented Generation, prompts include:

• User query
• Retrieved context
• System instructions

Compression is essential to fit everything into context.

Use only the provided context to answer.
If missing, respond "Not found".
Context:
{{documents}}
Question:
{{query}}
  

This compressed prompt enforces strict grounding with minimal tokens.

What Not to Compress

Never compress:

• Safety constraints
• Output format rules
• Critical domain instructions

Those must remain explicit.

Best Practices

Effective prompt compression:

• Removes repetition first
• Uses lists and labels
• Tests output equivalence

Practice

Quick Quiz

Recap: Prompt compression optimizes token usage without sacrificing intent or control.

Next up: System prompts — controlling model behavior at the highest level.