PromptRouter

Prompt Search + AI DNS

PromptHub introduces a novel semantic resolution layer for AI logic called PromptRouter, which functions similarly to Domain Name System (DNS) but for model-agnostic prompt modules. This allows agents, dApps, and users to locate and invoke the most relevant prompt logic by querying intent or context rather than hardcoded IDs.

1. Semantic Routing Layer

Instead of referencing prompts by address or contract ID, PromptRouter supports semantic resolution using:

• Natural language query matching

• Tag-based filtering (e.g., domain, output type, intent category)

• Prompt function signatures (input/output schema typing)

• Execution success rate and ranking weights

The router dynamically returns the best-matched registered prompt module, ranked by trust score, execution reputation, or governance weight.

2. AI DNS Analogy

PromptRouter behaves like a decentralized DNS:

• Prompt IDs = Hostnames

• Routing Context = Resolver logic

• Execution Bindings = A-record equivalents (execution endpoint)

This makes it possible to resolve abstract calls like:

{
  "intent": "summarize legal document",
  "output_format": "markdown"
}

And receive:

{
  "prompt_id": "summarize_legal_md@v1.3",
  "vault_uri": "ipfs://...",
  "fee": 0.5 PHUB
}

3. Decentralized Prompt Discovery

By using PromptRouter, the ecosystem supports plug-and-play AI services without requiring developers to hardcode module addresses. This enables:

• Modular prompt resolution in agent pipelines

• Dynamic fallback logic if one module fails

• Context-aware specialization (e.g., region-specific outputs)

• Private/internal prompt resolution within DAOs or orgs

PromptRouter is the missing registry-and-resolution layer that transforms the prompt ecosystem from static text to dynamically invocable, trust-weighted logic networks.