Promptspace

Think of prompt chains as interconnected LLM calls - each processing data and passing it along. When you’re working with 20 different prompts across files, you can’t see how they interact or where to optimize. That’s the core technical challenge - finding ways to visualize these complex interactions intuitively.

I built specialized nodes to handle different data types - table nodes for structured data, text nodes for prompts, eval nodes for testing. Using React Flow and Next.js, you can instantly see how data transforms as it flows through your chain. The interesting engineering problems came from making complex data easily visualizable and actionable in the UI.

Each node tracks crucial metrics - token usage, latency, cost - exposing performance issues instantly. The backend manages template versioning and chain validation, while we run extensive testing on nodes and full chains.

On the surface, it’s just drag-and-drop simple with nodes and connections. But underneath, there’s a lot going on to make these prompt chains reliable and efficient.