Reduce LLM API Costs with Prompt Optimization

Last month my startup was burning $1,200/month on Claude API calls. After profiling our prompts with Prompt Optimizer, I found we were sending 2,100-token requests when 1,200 tokens produced identical results. Here's exactly what we changed.

Step 1: Measure Before You Optimize

Run each prompt through the token counter. We found three patterns eating tokens:

• Repeated examples (we sent 5, needed 1)
• Verbose system prompts explaining the AI's job (it already knows)
• Multi-sentence context that single words covered

Step 2: Apply Classification Lock

For sorting tasks, strip the narrative. Before: 340-token preamble. After: 18 tokens. Result: same accuracy, 95% cheaper on that prompt class.

Our context detection engine automatically identifies which "Precision Lock" applies:

• code_generation → structured_output, concise_examples
• summarization → brevity_first, key_facts_only
• classification → binary_output, no_explanation

Step 3: Track Weekly

Set up a weekly diff of average tokens per request. We went from 2,100 → 1,200 in 3 weeks. At the end of the month: $720 instead of $1,200. That's a 40% reduction.

Results

| Prompt Type | Before | After | Savings | |-------------|--------|-------|---------| | Code gen | 1,800 | 900 | 50% | | Summarize | 2,400 | 1,400 | 42% | | Classify | 500 | 80 | 84% |

The key insight: context detection + precision locks eliminate the guesswork from prompt engineering. Try Prompt Optimizer at https://promptoptimizer.xyz to see your savings estimate.