Most teams try to reduce AI costs by switching to cheaper models or reducing usage. But the highest-leverage optimization is almost always overlooked: fixing the prompts themselves.
We reduced API costs by 40% on a customer support classification task — without changing models, without reducing volume, without sacrificing accuracy. The only change was how we structured the prompts.
The problem: expensive, unreliable classification
Our starting point: using an LLM to classify customer support tickets.
The raw prompt: “Classify this support ticket: [ticket text]. Return urgent, normal, or low.”
- ~800 tokens per request (unnecessary explanation)
- 15% misclassification rate
- 2.3 retries average
- Effective cost: ~2,400 tokens per ticket
The fix: three techniques, applied systematically
1. Structured output
Added explicit format instructions: return only a JSON object with priority, confidence, and reason.
Impact: Eliminated format errors. Token usage dropped from ~800 to ~500.
2. Few-shot examples
Added three labeled examples — one for each priority level.
Impact: Misclassification dropped from 15% to 2%.
3. Role prompting
Added: “You are an experienced customer support triage specialist who has processed over 100,000 tickets.”
Impact: More consistent tone and calibration.
The results
| Metric | Before | After | Change |
|---|---|---|---|
| Tokens per request | ~800 | ~500 | -37% |
| Misclassification | 15% | 2% | -87% |
| Retries per ticket | 2.3 | 0.1 | -96% |
| Effective tokens/ticket | ~2,400 | ~500 | -79% |
| Total API cost | Baseline | -40% | Significant |
Why this works across use cases
The same pattern — structured output + few-shot + role prompting — applies to code review, data extraction, content generation, and report generation.
The principle is universal: tell the model exactly what you want, show it examples, and give it the right context.
Testing across models matters
Cost savings varied significantly by model. The optimal choice depends on your priorities — accuracy, cost, speed. The only way to know is to test.
PrismForge’s Multi-LLM Test Lab lets you run this comparison. Build your prompt with techniques applied, test across 7 models, and pick the winner based on data.
Pay for what you use. Engineered prompts make every token count.