Name: Google's 6 Hour Prompt Engineering Course in 10 Minutes
Uploaded: 2026-01-22T23:47:27.000Z
Duration: 10 min 55 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

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I just finished Google's six hour prompt engineering course and it is hands down the best AI training I have ever taken.

So I'm saving you the time and I've packed every essential tactic Google revealed into this guide so you can master the entire system in under 12 minutes.

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Of course, I can't fit every lesson from this nine hour course into one video, so if you want the complete training and an official certificate from Google you can actually put on your resume or LinkedIn.

I'll leave a link to Google's course in the description.

It starts with understanding how the model actually thinks.

So Google structures their entire course around five core task context references.

Evaluate and iterate the foundation of everything is task.

This is simply what you want the AI to do.

Not the general topic, but the exact output you need.

A bad task is help me with email, while a good task is reformatting the sentence to write an email to my gym staff about a schedule change.

But here's where Google takes it further.

You can add two elements to make the task even stronger.

When you tell the AI to act as an expert, you aren't just playing pretend.

You are priming the model to access a specific set of vocabulary and logic.

adding the instruction like act as a physical therapist ensures you get safety tips and anatomical focus rather than just a list of exercises.

It changes the entire vibe of the answer.

Second is format, and this is your biggest time saver.

If you don't define the format, the AI defaults to a generic wall of text.

By asking for a bulleted list, a markdown table, or even a JSON snippet.

You force the AI to organize its thinking.

You stop getting raw information that you have to fix later and start getting usable deliverables that are ready to.

These two additions turn a basic task into a specific structured result.

Once the task is clear, you need to layer in context.

Context is the background data that steers the model.

The more information you provide, the less the AI has to guess.

If you just type write landing page copy for my website, you get generic text that could apply to anything.

But look at what happens when you inject context.

I'm building a project management tool for freelance designers.

The Target users are 25 to 40 and they are frustrated with tools like Asana being too complex.

My product focuses on visual timelines and client portals.

Now the AI knows exactly who you are talking to and the output becomes targeted instead of generic.

Google's course emphasizes this repeatedly.

They assume the AI will figure it out, but it won't.

Now, to really lock in the quality, you add references.

References are examples that show the AI what you're aiming for.

Sometimes words aren't enough to capture a specific vibe or structure.

Let's say you are writing a product description and you need it to match your specific brand voice.

Paste in three of your best descriptions and tell the model.

Write a new description using the same style as these examples.

Or, if you are creating social media content, feed it your top performing posts and tell the AI to analyze why they worked.

Then have IT generate new posts that follow that exact pattern.

References turn vague instructions into concrete targets.

It stops the model from guessing your style and forces it to match what you already know.

Once you get a response, you move to evaluate.

This means checking if the output actually hits the mark.

It sounds basic, but this is where most people fail.

They skim the text, settle for good enough, and move on.

You need to actively verify that the output matches the task, hits the right tone, and

Google provides four specific ways to fix a broken prompt.

The first method is to simply revisit the framework.

Go back to the start and check if you missed the context or forgot to assign a Persona.

Sometimes the fix is just filling in the gaps you missed the first time.

If that doesn't work, try the second method.

Breaking into simpler sentences the AI processes information just like a human does.

If you dump a massive paragraph of instructions, it gets overwhelmed.

Don't write a run on sentence about a Q1 strategy targeting Gen Z with budgets and KPIs all in one breath.

Break it down and write something like Create a strategy for Q1 target Gen Z, include a budget, add KPIs.

Writing the same information but with clearer structure results in better output.

The third tactic is to use analogous tasks.

If the direct approach fails, try a different angle.

If write a business proposal is giving you dry, boring results, switch the frame.

Ask it to write a persuasive argument for a partnership.

You are changing the mental model the AI uses, which often gives a much better result.

If you ask for video ideas and get generic results, clamp down on the requirements.

Now the AI has to work within a box, which makes the ideas specific instead of broad.

Now beyond text, there is multimodal prompting.

This is where models like Gemini really separate themselves from the pack as besides just reading text, they can process images, audio and video natively.

Say you are redesigning a website and need feedback.

Instead of wasting time describing the layout in words, just upload a screenshot.

Then command it to analyze this homepage design.

Identify three specific areas where user attention might drop off and suggest improvements.

Or if you are a musician working on a track, upload the audio file and ask it to describe the mood of this piece.

Then suggest five alternative directions.

The framework still applies here you are just replacing vague text descriptions with high fidelity visual or audio references.

But we need to address the elephant in the room.

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Google's 6 Hour Prompt Engineering Course in 10 Minutes

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