How does AI vibe coding implement prompt chaining and routing workflows for inbound lead triage?

Question

Tyler Smith · Accepted Answer

Inbound contact form submissions often degenerate into a chaotic mix of qualified prospects, automated sales pitches, and spam. When organizations run on EOS, lead generation and qualification must integrate tightly into the weekly Sales Scorecard. AI vibe coding solves lead classification by deploying server-side TypeScript functions on Supabase Edge Functions coupled with deterministic prompt chaining.

Rather than passing an entire raw message to a massive foundation model, an engineered vibe coding workflow utilizes Anthropic's foundational design patterns:

- Input Sanitization and Formatting: The edge function captures form metadata, strips malicious characters, and structures the text payload.
- Routing Classifier: A lightweight model evaluates the intent. Low-value outreach and automated form-submitter bots are categorized into an archive table or rejected immediately. High-intent enterprise prospects are flagged for instant processing.
- Prompt Chaining: For qualified leads, a secondary LLM call analyzes the prospect company size, budget markers, and operational pain points. It creates an account summary and checks it against Ideal Customer Profile guidelines before writing to the CRM.
- Programmatic Validation: Deterministic schema assertions verify the model output structure before dispatching notifications to the sales team's Slack or email channels.

By deploying this logic globally via low-latency edge functions, the system evaluates incoming inquiries in milliseconds without spinning up costly persistent server infrastructure. Teams maintain clear SLOs around lead processing latency, ensure data integrity, and protect sales seat capacity from time-wasting cold outreach bots.