Prompt Engineering 2.0: How to Cut LLM Costs by 60% Without Losing Accuracy

LLM Prompt Optimization for Cost and Accuracy Companies experimenting with Large Language Models (LLMs) often face a silent cost killer: inefficient prompts. Every redundant word or poorly structured instruction increases API tokens, latency, and total monthly spend. Prompt engineering is the systematic process of crafting, testing, and refining prompts to maximize accuracy and minimize cost. At Smaltsoft, our smalt prompt tool automates that optimization. It evaluates different prompt variants, measures performance across real use cases, and identifies the most cost-effective patterns. Typical results? Up to 40–60% reduction in API spending, while improving response precision and consistency. Optimized prompts also reduce the need for complex post-processing, cutting engineering overhead. For teams using GPT, Claude, or local LLMs, structured prompt optimization is no longer optional—it’s the new DevOps for AI. Bottom line: You don’t need to spend more on AI to get better answers. You need smarter prompts. → Test smalt prompt with your data today.