From Prompt to Paycheck: Wiring an LLM Chain Into Real Gig Platforms

说明与用途

将LLM链集成到真实的微任务平台，实现自动评估盈利、执行任务、验证结果并结算支付的完整工作流。

问题

Deploying an LLM as a revenue‑generating gig worker requires stateful execution, economic feasibility checks, and reliable settlement with marketplace APIs.

结果

The agent achieved a profit margin of at least $0.05 per task, automatically resuming after crashes, and reliably settled payments with the mock gig platform.

工作流

• Poll gig platform for new jobs via API or webhook
• Filter jobs by economic feasibility (payout minus token cost)
• Lock task to prevent duplicate claims
• Construct system prompt with task constraints
• Invoke LLM chain to generate output
• Parse structured output with pydantic
• Validate output against acceptance criteria
• Submit delivery to platform
• Handle payout callback and record transaction

证据

Below is a complete, production‑grade Python implementation of an agent worker.

来源

The system comprises three core pipelines: Ingestion, Execution, Settlement & Validation.

来源