Tutorials Prompt Engineering Tutorial
AI Cost Optimization — Complete Guide
AI Cost Optimization — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
On this page
Prompt Engineering Tutorial · Lesson 84 of 100
AI Cost Optimization
Prompts ✓ → Apps
Apps · 2 — RAG & agents · ~10 min · Module 9: Performance & Optimization
What is this?
Cost optimization picks cheaper models for easy tasks, caches, batches, and limits retries — tokens are not the only lever.
Why should you care?
PromptVerse routes obvious FAQ to mini model; hard reasoning to flagship only when confidence low.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
route(task):
if task.type == "faq" and retrieval_score > 0.9:
return llm_mini(task)
if task.type == "reason" or mini.confidence < 0.7:
return llm_flagship(task)
return llm_mini(task)What happened?
- High retrieval FAQ does not need flagship model.
- Escalate to expensive model only on low confidence or hard task type.
Practice next
- Tag tasks easy vs hard in logs.
- Measure cost per task type.
- Route easy to mini.
- Batch embed jobs off-peak.
- Set per-tenant monthly budget hard stop.
Remember
Model routing by difficulty. Cache + batch where safe. Cap retries and samples.
Model tiering
LLM bill unsustainable.
Outcome: 80% traffic on mini; quality eval flat.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
Sign in to ask a question or upvote helpful answers.
No questions yet — be the first to ask!
