# How to run your first AI UGC campaign (step-by-step guide)

## Executive summary

This guide details an automated, multi-stage workflow for generating hyperrealistic User-Generated Content (UGC) at scale, significantly reducing costs from $20-$50 per video to mere cents per second. The process involves analyzing trending content to identify effective hooks and characters, using advanced AI prompting techniques (like JSON output) to create digital characters with specific 'vibes,' and finally generating the video using specialized APIs (such as Cance 2.5) combined with audio cloning and automated scripting.

## Key takeaways

- UGC Strategy Foundation: Effective UGC content requires two parts: a strong hook and a product demo. Scaling involves pairing one product demo with multiple variations (5-10) of hooks to test performance.
- Character Generation Techniques: To generate a character with a specific 'vibe' while maintaining realism, it is crucial to restrict the model's exploratory space by using JSON-based prompts. This prevents the 'AI slop' look often associated with default model outputs.
- Model Comparison for Realism: While multiple models (e.g., GPT image 2.5, Gemini 3 Pro) can generate characters, the speaker claims Gemini 3 Pro currently yields the most super-realistic results for character generation.
- Automated Video Production Workflow: The full workflow uses a tool like 'track' to pull trending data, create a character image, and then use the Cance 2.5 model (via a restricted endpoint) to generate the final video. The process can be automated using a dedicated skill that handles scripting, voice cloning, and captioning.

## Technical details

- AI Tooling and Data Access: The workflow relies on 'track,' described as an open-source project and an 'OpenRouter for data & tools,' allowing agents to access various data and services without paying per subscription, only for usage.
- Character Generation Prompting: To achieve high realism, the speaker recommends using JSON-based prompts to constrain the model's output and define every detail, rather than relying on default prompts.
- Video Generation API Bypass: The Cance 2.5 model typically restricts hyperrealistic image usage to prevent impersonation. The speaker suggests using a specialized API (like the one provided by 'track') to bypass these restrictions at a lower cost.
- Audio Enhancement: To ensure the final video has a natural rhythm and speech feel, the speaker recommends passing an audio reference clip from an original, successful video, rather than relying solely on text-to-speech.

## Practical implications

- Automating the entire UGC content pipeline, from market research (finding trending hooks) to final video rendering.
- Implementing cost-saving measures by utilizing specialized, restricted APIs to bypass default model limitations and reduce generation costs significantly.
- Building a multi-agent system that coordinates data scraping, character generation, and video synthesis for scalable content production.
- Integrating voice cloning (e.g., ElevenLabs) and audio reference passing to improve the naturalism of synthetic video content.

## Topics

AI Content Generation, Workflow Automation, Generative AI, Video Synthesis, Prompt Engineering, OpenRouter for data & tools, portrait-clone skill, taking-head-video skill, AI Builder Club Workshop

Source: https://www.youtube.com/watch?v=GkGufbIVVC8