Edward JobsIntroduction: “Google Opal” Is Redefining the Norms of AI App Creation
Google’s new tool Google Opal, which lets you build AI mini-apps with no code, has arrived—ushering in an era where you can compose, edit, and share workflows in natural language. It has drawn attention in official introductions and domestic media, and reports say it became available in Japan in October 2025. 1
In this article, using a verification video from the YouTube channel “KEITO【AI&WEB ch】” as a reference, we’ll explain— with an emphasis on reproducibility—how to build a workflow that automatically generates a blog post from a YouTube video. 2
What Is Google Opal? A New No-Code Tool for Harnessing AI
Opal is a tool that turns multi-step AI workflows into working pipelines just by describing your requirements in text, and lets you preview, edit, and share them. Provided as an experimental project from Google Labs, it excels at idea validation and business prototyping. 3
You can further extend it in the editor by combining Assets (add YouTube or files), Tools (such as web search), and Generate (integrations with Gemini/Imagen/Veo). You can also express Japanese output control and conditional branching in natural language. 4
Hands-On! An AI App That Auto-Generates a Blog Post from a YouTube Video
Below is a workflow design distilled from the video demo that you can reproduce using standard Opal features. 2
Overview of the Workflow
| Step | Processing | Purpose |
|---|---|---|
| 1. Input | Receive the YouTube video URL via a form | Specify the execution trigger and data source |
| 2. Fetch Data | Add YouTube via Add Assets (reference with @) | Access the video’s content / captions / key points |
| 3. Summarize & Structure | Extract key points with Generate (Gemini) → convert to H2/H3 headings | Convert to an SEO-friendly logical structure |
| 4. Draft Article | Use Generate again to create a body draft (specify tone, length, and internal-linking policy) | Shape it into a readable article |
| 5. Finalize | Use Tools (web search) to fill gaps → instruct additions | Fact-check and augment |
| 6. Output | Produce Markdown/Rich Text for Google Docs | Provide an easy-to-edit and publishable format |
Prompt Example (Step 3: Summarize & Structure) “You are a blog editor. Read @YouTubeAsset and create H2/H3 heading ideas that satisfy the search intent (intro for beginners → steps → caveats). Bullet the key points for each heading in three lines. Write in Japanese.”
Prompt Example (Step 4: Draft Article) “Following the heading structure above, write 1,500–2,000 characters in natural Japanese in the order: introduction → steps → use cases → caveats → conclusion. Avoid first-person ‘experience’ assertions; keep it fact-based. Add a checklist at the end.”
Handling of YouTube captions and audio content varies by video. If captions are sparse, it’s practical to supplement with the video description and related materials using Tools (Search web). 4
Implementation Tips (Checks to Boost Reproducibility)
- Japanese Output Control: Explicitly specify the output language (“Always in Japanese”). If needed, add a translation step midway. 4
- Natural-Language Branching: You can describe conditions in prose, e.g., “If no captions are found, extract key points from the video description.” 4
- Templatize: Make your heading template and final checklist separate steps so they’re reusable across other videos.
- Preview-Driven Ops: Use Preview → Console to inspect logs and pinpoint bottlenecks. 4
Other Use Cases: Batch Image Style Variations / Slide Creation
- Batch Image Style Variations: Automatically generate multiple variations—such as anime, retro, or watercolor—from a single image (implementable via prompt branching with Imagen integration). The demo video also showcases successful examples. 2
- Slide Creation: You can attempt to generate draft content for Google Slides from a topic and target audience. However, current output tends to be text-centric with minimal design. The practical approach is to finish visual design later with a specialized tool. 2
Conclusion: Google Opal Upgrades the “Speed of Making”
Opal, which lets you create AI mini-apps using only natural language, is powerful for automating repurposing from video to article. By handing it a YouTube URL and running key-point extraction → structuring → article generation → output end-to-end, you can dramatically improve production throughput. With domestic rollout underway and a low learning curve, start with small automations and scale horizontally through templating. 5
References & Related Links
【Beginner-Friendly】Tried Making an AI App Quickly with Google Opal (video transcription app, image style changes, etc.) ↩︎ ↩︎ ↩︎ ↩︎
Introducing Opal: Describe, Build, and Share AI Mini-Apps ↩︎
How to Use Google Opal: The Fastest Way to Build AI Mini-Apps with No Code ↩︎ ↩︎ ↩︎ ↩︎ ↩︎
“Opal,” a Tool for No-Code AI Mini-App Creation, Now Available in Japan ↩︎
