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Cost Optimization

Master strategies to reduce AI agent costs while maintaining performance quality

Optimizing Token Usage

Every token costs moneyβ€”both input and output. A 2,000-token prompt with a 500-token response costs 2.5x more than a 1,000-token prompt with the same response. Token optimization is about communicating the same information with fewer tokens while maintaining quality.

Token Optimization Principles

Remove Conversational Fluff
Strip unnecessary politeness, explanations, and verbose instructions. Be direct.
30-50% token reduction
Use Abbreviations & Shorthand
Replace verbose phrases with concise alternatives. Models understand context.
20-40% reduction
Minimize Few-Shot Examples
Use 1-2 examples instead of 5+. Models generalize well from minimal examples.
40-70% reduction
Request Structured Output
Ask for JSON/CSV instead of prose. Structured formats are more token-efficient.
20-30% reduction

Interactive: Prompt Optimizer

Paste your prompt and see how to optimize it:

Before & After Examples

Remove Conversational Fluff
❌ Before:
You are a helpful assistant. Please kindly analyze the following text and provide a detailed summary...
βœ“ After:
Summarize this text:
85% fewer tokens
Use Abbreviations & Shorthand
❌ Before:
Extract the following information from the document: name, email address, phone number, company name
βœ“ After:
Extract: name, email, phone, company
60% fewer tokens
Minimize Few-Shot Examples
❌ Before:
5 examples Γ— 100 tokens each = 500 tokens
βœ“ After:
2 examples Γ— 100 tokens each = 200 tokens
60% fewer tokens
Request Structured Output
❌ Before:
The name is John, his email is john@example.com, and he works at Acme Corp.
βœ“ After:
{"name":"John","email":"john@example.com","company":"Acme"}
40% fewer tokens
πŸ’‘
Test Quality After Optimization

Always validate that shorter prompts maintain output quality. Run A/B tests comparing original vs optimized prompts on a sample dataset. If quality drops <5% but costs drop 40%, that's usually worth it. Track metrics: accuracy, user satisfaction, task completion rate.

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