An AI-powered system that automates podcast promotion workflows using GPT-4 and structured prompt chains.
Built with agent-first architecture to generate short-form clips, social captions, metadata, and thumbnail prompts from long-form content — reducing production time while preserving brand voice and emotional resonance.

PromoFlow AI accelerates podcast content workflows by auto-generating ready-to-publish assets from long-form video/audio.

This semi-automated system blends large language model (LLM) capabilities with human-in-the-loop QA to ensure quality, tone alignment, and rapid turnaround.

Problem
Producing marketing assets from weekly podcast episodes was time-consuming and inconsistent — requiring multiple team members to manually extract highlights, write captions, and format deliverables.

Solution
Designed a modular AI workflow that parses transcripts, identifies key moments, and generates clip selections, titles, captions, and thumbnail prompts in a consistent JSON format — all routed through a human QA checkpoint.

Results

• Reduced asset production time by 60%, saving 4–6 hours per episode
• Enabled faster go-to-market for weekly podcast releases
• Improved handoff clarity between creative and marketing teams
• Ensured consistent branding across platforms without additional headcount

• Extracts timestamps and themes from podcast transcripts
• Suggests 3–5 emotional hooks for short-form content
• Produces titles, captions, and thumbnail prompts tailored to social platform styles
• Generates Classic, Hybrid, and Modern caption styles per client brand voice
• Exports JSON handoff format for downstream automation
• Supports human-in-the-loop QA to refine before publishing

Built with structured prompt chains and Chain-of-Thought reasoning to enhance consistency and interpret nuance:

• Clip logic: Detects emotional tone and topic transitions to suggest high-retention segments
• Fallback behavior: Requests missing metadata instead of hallucinating when episode inputs are incomplete
• Style variation: Applies formatting and tone parameters for different social media personas
• Title optimization: Uses emotional resonance scoring + packaging prompt chains

• GPT-4 (ChatGPT) – prompt-driven generation engine
• NoteGPT + YouTube Transcript API – source ingestion
• n8n – automation and routing
• JSON – structured delivery format
• Google Sheets + Notion – content pipeline and team handoff
• Frame.io – video editing feedback loop
• (Claude used for prompt testing and alt caption generation)

• ✅ Live in weekly production
• 🟡 Refining prompt logic and JSON formatting
• 🔴 Automation layer + public repo polish (in progress)

• Smarter clip detection based on episode themes and emotional arcs
• End-to-end automation: JSON → publishing tool integration
• AI-generated thumbnails and auto-packaged delivery folders

This is a solo case-study project designed for AI workflow architecture.
Suggestions and feedback welcome.