Today we’re launching Prompt Adaptation, a state-of-the-art agentic system that automatically adapts prompts across LLMs. Prompt Adaptation outperforms all other methods and significantly improves accuracy over manual prompt engineering, saving you thousands of hours per year.

Nine months ago, we released the world’s most powerful model router, outperforming every foundation model on every major benchmark. But choosing the right model is only half the battle—we also have to know how to prompt them. Doing this manually is extremely time-consuming.

Prompt Adaptation addresses this critical issue by automatically adapting prompts across models, replacing 25+ hrs of manual prompt engineering with 30m of background processing. We outperform all other evaluated techniques including Meta DSPy and Bedrock Prompt Optimization.

Prompt Adaptation is a black box prompt optimization technique (BPO) similar to that pioneered by DSPy. It takes your original prompt and a set of golden inputs and outputs from your application and then iterates over thousands of potential prompts to find the best prompt for each model.

Our enterprise customers have already begun to see the benefits. At his Sapphire keynote today, SAP’s CTO Philipp Herzig previewed a new Prompt Optimization Service leveraging Not Diamond with the intent of driving accuracy improvements and accelerating engineering throughput.

I’m also excited to announce additional funding from @defyvc, @IBM, Fund, @MyriadVC, @deepwatermgmt, @dnxventures, and @AmbushCapital to continue building a world class team (have never worked with a better team in my life), and it’s an honor to have such an incredible crew joining the cap table.

The future is multi-model. You wouldn’t rewrite your codebase every week, and you shouldn’t have to rewrite your prompts either. If you want to automate your prompt engineering and save 1000 hours this year, I'd love for you to try it out. Sign up at

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@cyrusnewday

Hey congrats

This looks like a serious time-saver. Curious to see how it plays out in real-world use, feels like a big step forward. How do you see this benefiting solo builders or early-stage founders?

When working across multiple AI models, subtle prompt changes make huge differences. This solves a common frustration: what works beautifully in GPT may flop in Claude. Quick DIY approach until you get access: "Rewrite this prompt specifically for [Model X], considering its strengths in [specific capability] and tendency to [known behavior]: [paste prompt]" Track which models excel at which tasks in a simple spreadsheet. The patterns will surprise you.