🏴‍☠️ Gemini 3, Six-Step Prompts

Flash vs Pro Explained

Author

Captain YAR
January 14, 2026 • Estimated Reading Time: 8 minutes

Everyone assumes the current AI tools are as good as it gets, but this latest update completely shatters that ceiling.

I’ve been keeping an eye on Google’s developments, but the capabilities described in this new breakdown are staggering. The original poster, an expert who helps entrepreneurs streamline their workflows, took Gemini 3 for a spin and described the experience as simply “unreal”!

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The Engine Under the Hood
So, what actually sets this apart? According to the breakdown, Gemini 3 isn’t just a standard update; it’s a major leap forward from Google DeepMind. It’s built for state-of-the-art reasoning and advanced coding, with the real power coming from multimodal understanding.

It can process text, images, audio, and video in the same workflow. The author notes it comes in variants like Pro for deep thinking and Flash for speed. It’s also integrated directly into Google Search’s AI Mode.

The Six-Step Framework
One of the most valuable takeaways is a structured framework for communicating with the model. Instead of throwing random questions at the bot, the expert outlines a six-point strategy. It starts with defining a Role and a Task, then adds Context, Reasoning logic, Output formats, and Stop conditions.

This structure forces clarity before the model starts working. It helps the AI validate its own steps instead of guessing what you meant halfway through. It also makes the endpoint explicit, so the model knows exactly when the job is done.

Optimizing for Success
For execution, the creator recommends leaning into what Gemini 3 does best. Because it has improved factual accuracy, you can push it into complex, multi-step workflows like coding and strategic planning. The advice is to be concise but ambitious, and to stack steps instead of burying them in one messy paragraph.

The multimodal angle is a big time-saver. If you have visuals, let the model analyze them directly rather than translating everything into text yourself. The claim is simple: done right, this can cut hours of manual work.

Safety and Guardrails
It’s equally important to know what not to do. The LinkedIn user warns against overcomplicating prompts with conflicting contexts. Don’t give it different tasks with different contexts in a single breath.

Privacy is another focus. Even with improved security features, you should avoid sharing sensitive private data. Treat prompts like they could be seen outside your intended audience.

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Prompt of the Day
The post’s author shared this specific template to utilize the framework mentioned above. Here is the exact text to copy:

Act as [Role] to [Task].
Begin with a checklist of 3–7 steps to plan how you’ll complete the task.
Ensure the content is accurate, unique, and excludes [things to avoid].
Prioritize clarity and practical value when presenting results.
Internally validate and cross-check information before finalizing.
Return the results in this format: [desired output format].
The task is complete when [stop condition].

The Learning Curve
This tool may be powerful, but it demands better inputs. You can’t throw lazy queries at a model like this and expect perfect results. You have to learn the framework and use it consistently.

The point is that the “unreal” upgrade only benefits you if you stop using old, vague prompting habits. Clear roles, clean context, and hard stop conditions turn the model into a system instead of a slot machine.

To see the full infographic and more details on the “Flash” vs “Pro” models, check the link!

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