Editorial Policy and Review Methodology

Last updated: April 12, 2026

Happy Horse publishes product documentation, workflow guidance, and public-facing explanatory content related to AI video generation.

This page explains how that content is evaluated, updated, and maintained.

What We Cover

We publish content related to:

• text-to-video workflows
• image-to-video workflows
• prompt iteration and generation controls
• product capabilities, constraints, and output expectations
• practical use cases such as ads, product promos, and social video drafts

How We Evaluate Information

When we describe the product or publish workflow guidance, we look at factors such as:

• whether the workflow can be reproduced on the live product
• how much setup friction is required before a user gets a usable result
• what kinds of outputs the product is actually good at
• where the product still has limitations or rough edges
• whether the guidance is useful for real production scenarios rather than generic AI claims

How Updates Happen

We update public content when:

• the product workflow changes in a meaningful way
• core capabilities are added, removed, or repositioned
• pricing or access conditions change
• previously published descriptions become materially outdated
• a factual issue is reported and verified

Where relevant, pages should include updated dates so readers can judge freshness.

What We Try to Avoid

We try to avoid:

• overstating product maturity
• describing speculative features as generally available
• hiding important workflow friction
• publishing copy that sounds impressive but is not operationally useful
• leaving outdated product descriptions in place after the workflow has changed

Disclosure and Independence

If sponsorships, affiliate relationships, or paid placements are introduced in the future, they should be disclosed clearly on the relevant page.

Our goal is clarity, not hype. Public-facing copy should help users understand what the product can do, where it works well, and what trade-offs still exist.

Corrections and Feedback

If you notice a factual issue, outdated workflow description, or broken product claim, contact:

• support@happyhorsesai.app

Editorial Summary

Our editorial standard is simple:

• practical over vague
• workflow-tested over assumption-driven
• explicit trade-offs over blanket claims
• useful product clarity over generic AI marketing language