i burned 7,000 higgsfield credits so you don't have to (Free Prompt Guide)
Highlights the critical role of prompt engineering and credit management for sustainable AI filmmaking at scale.
What this lesson covers
Joey recounts burning 7,000 Higgsfield credits while building a fully AI‑generated K‑Pop group and short film, then shares a complete prompt library, character sheet system, and cost‑saving tactics. The video reveals how Claude’s director skill and disciplined prompt engineering can dramatically improve efficiency and visual consistency.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 00:00 ↗
Tracking credit usage reveals hidden costs of AI video generation.
- 00:45 ↗
Building six‑panel character sheets prevents visual drift across generations.
- 01:05 ↗
Single‑paragraph prompts ordered by style, mood, and dynamics improve consistency.
- 02:16 ↗
Training Claude with a director skill and script bible enables automated, sharper scene generation.
Workflow outlined in the video
- Log credit consumption per model to monitor costs.
- Create comprehensive six‑panel character sheets (front, side, three‑quarter, hands, nails).
- Write concise, single‑paragraph prompts ordered by style, mood, then dynamics.
- Load prompts into Claude’s director skill to generate detailed scene descriptions.
- Feed generated prompts into Seedance (or chosen video model) for footage creation.
- Review output and iterate prompts to maintain character consistency.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
Explore the tools
How to interpret the numbers
26,029 views captured 24 October 2026. ReelStack recommendation score: 41/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 65% performance: 0.96× current views vs the channel's recent median; same-age history is not yet available. View-evidence factor 96% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
- 10% engagement: 100/100; likes + 4× comments, smoothed with a 500-view neutral prior