Tutorials Prompt Engineering Tutorial
Token Optimization — Complete Guide
Token Optimization — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 81 of 100
Token Optimization
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 9: Performance & Optimization
What is this?
Token optimization trims prompts and outputs to the minimum text that preserves quality — direct savings on every API call.
Why should you care?
PromptVerse bills tenants partly on tokens; support templates were shortened 40% without accuracy loss.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
before = long_system_prompt_900_tokens
after = bullet_system_220_tokens # same rules, fewer adjectives
user = structured_fields_not_paragraph # saves ~30%
max_tokens = 256 # cap reply lengthWhat happened?
- Bullet system prompts encode same rules compactly.
- Structured user fields beat narrative.
- max_tokens stops rambling completions.
Practice next
- Measure tokens on one production prompt.
- Rewrite system as bullets.
- Convert user blob to labeled fields.
- Remove duplicate instructions in user+system.
- Cache static system prefix where provider allows.
Remember
Compact system bullets. Structured user input. Cap completion tokens.
Support cost down
Token spend 2× forecast.
Outcome: Compact prompts cut monthly LLM bill 35%.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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