I spent ~500M tokens building a prompt optimization tool
Article summary
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Hey everyone, I've been working on an automated prompt optimization project for a while now, and I've gone through roughly 500M tokens iterating on the core loop. Along the way, I tried leaning on pretty much every major model out there — GLM, DeepSeek, GPT, Claude, you name it — to help me refine the architecture. But honestly, their output was underwhelming for this specific task. Most of their built-in agent/skill features were basically useless for actually designing a better optimization…
1Key Takeaways
- Hey everyone, I've been working on an automated prompt optimization project for a while now, and I've gone through roughly 500M tokens iterating on the core loop.
- Along the way, I tried leaning on pretty much every major model out there — GLM, DeepSeek, GPT, Claude, you name it — to help me refine the architecture.
- But honestly, their output was underwhelming for this specific task.
- Most of their built-in agent/skill features were basically useless for actually designing a better optimization….
2AIWedia Score
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that hey everyone, I've been working on an automated prompt optimization project for a while now, and I've gone through roughly 500M tokens iterating on the core loop.
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