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š“āā ļø Unlock Geminiās Power with Workflows
This Changes How You Work
Captain YAR
February 05, 2026 ā¢ Estimated Reading Time: 8 minutes
I used to think AI was basically a faster Google. Ask a question, grab an answer, move on. Then I watched this talented creator stack a few Gemini tools together and it honestly changed how I think about āusing AI.ā
The crazy part is that none of it depended on clever prompts. It was about building a repeatable setup that keeps getting smarter every time you use it. And once you see it, you realize most of us are leaving a lot of power untouched.
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Most people are using artificial intelligence completely wrong by treating it like a simple search engine or a basic chatbot. They type in a question, get an answer, and move on, but they are leaving about ninety percent of the platformās power on the table. In this breakdown, the creator argues the real magic is not in single features, but in how you combine them into workflows that produce real outputs, like plans, apps, and dashboards.
He also points out that while Google Gemini has quadrupled its market share recently, the real value is multimodality. In plain English, it can read, see, hear, and speak in the same flow. Instead of a āfeature tour,ā he shows how tools like Deep Research, Canvas, and Gems can be stacked so the result is not just an answer, but something you can actually use.
The Shift from Prompt Engineering to Context Engineering
One of the biggest takeaways is that āprompt engineeringā matters less than people think. This creatorās point is simple: stop hunting for magic words, and start feeding the model the right context.
He proves it by connecting Notebook LM to Gemini. Instead of asking for a script from scratch, he uploads transcripts from his top 25 YouTube videos plus his analytics. Now the notebook understands his voice, what his audience reacts to, and the patterns that already work. Then he connects that notebook to a Gem, which is basically a custom AI with instructions that stays consistent.
The result is the key: he does not have to re-explain his tone every time. He asks for a new intro, and the output is immediately usable because the AI is working from a real library, not guesses. You are not trying to trick the model into being smart, you are giving it what it needs to be smart about you.
Turning Visual Inputs into Interactive Apps
Another workflow shows how visual input can turn into a real tool. He uses a personal example: wanting to start a garden, knowing nothing, and feeling stuck. Instead of typing āhow do I garden,ā he uploads a photo of an empty backyard.
Gemini analyzes the image and factors in space and local climate in Utah. It generates a planting timeline, but then he pushes it further by asking for a dynamic interface. Using Canvas, it builds an interactive dashboard in the browser with a calendar, watering reminders, and harvest predictions.
Then he saves everything into a Garden Helper Gem. Now if he sees a drooping leaf or a strange bug, he snaps a photo and gets advice that matches his exact layout and plant list. The lesson is bigger than gardening: the output does not have to be text. It can be a custom tool you interact with.
Automating Drudgery with Gems
The third workflow is about killing repetitive admin work. He takes a photo of a messy receipt and asks Gemini to extract the date, items, amount, and category into a table. Even with crumpled paper, it reads the text cleanly.
To make it repeatable, he turns it into a Gem called Expense Tracker with strict formatting instructions. Now he does not write the prompt again. He just drops in a receipt photo and the system does the same job every time.
He even has it visualize the data by generating a simple tracking interface. The rule he shares is worth stealing: if you type the same prompt more than twice, you should probably build a Gem and turn it into a one-step workflow.
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From Idea to Investor Pitch in Minutes
The final workflow is the most ambitious: a full sprint from idea to pitch. He starts with a rough concept, a vegetable swapping app for gardeners with too much of one crop. He uses Deep Research to scan forums, Reddit, and YouTube comments to validate the problem, then researches competitors and monetization.
Next, he uses image tools for quick branding mockups and Canvas to prototype the app so it feels real. Finally, he feeds the research and prototype notes into Notebook LM to generate a pitch deck. The point is not that every deck will be perfect. It is that the bottleneck has moved, from āI canāt build thisā to āI need to orchestrate the tools well.ā
If you want to see the full breakdown of these workflows and exactly how the original creator set up his prompts, you have to check out the full post.
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