🧠 Google's free Prompting Guide—Made Stupidly Simple

Prompt Smarter, Not Harder (Straight from Google’s Playbook)

April 10, 2025

👋 Hey there,

Last week, we read Google’s Prompt Engineering whitepaper, so you don’t have to. If you’ve ever yelled at ChatGPT for being ‘mid’—this one’s for you.

Most people are getting bad AI results not because the model sucks… but because the prompt does.

We’re breaking down the most useful stuff from the 60+ page paper into bite-sized, actionable takeaways (with a side of sass). But more importantly, I’ll show you how to apply them—with real use-cases and prompts you can literally copy-paste.

And yes—we’ll also show you how to skip the tinkering and let Prompt Genie do the thinking for you.

Let’s break it down 👇

1️⃣ First, understand what a prompt actually is

  • You’re not just “asking ChatGPT a question.”

    You’re giving it instructions. You’re feeding it context. You’re shaping its identity. You’re programming a model using language.

    And the way you do that changes everything. Here’s how:

🧩 Framework:

Role + Task + Structure + Constraints = Gold

Both are about retro games, but notice how different they sound? That’s the power of the prompt. The way you frame your question changes the kind of answer the AI gives you—more detail, more personality, or just a quick summary. It all depends on what you ask.

2️⃣ LLM Settings (aka how you control the vibe)

  • Large Language Models (LLMs) like ChatGPT don’t just spit out random words—they generate outputs one word (or token) at a time based on probabilities. But here’s the fun part: you can tweak how they do that.

    Here are the big 3:

    1. Temperature â€“ Controls creativity vs. precision.

      • Low temp (e.g., 0.1) = logical, predictable output

      • High temp (e.g., 0.9) = imaginative, unpredictable output

    2. Top-K â€“ Limits the number of “next word” options.

      • Top-K = 5? It chooses from the top 5 likely next words only.

      • Higher K = more diverse outputs; lower = tighter focus.

    3. Top-P â€“ Looks at the smallest set of words that make up P% of total probability.

      • Top-P = 0.9? The model will only consider words that collectively have a 90% chance of being used next.

    Think of it like this:

Setting

Controls

You’d Use It When You Want

Temperature

How wild or safe the output is

More creativity or more logic

Top-K

Number of choices to consider

More or less variety in style or wording

Top-P

How confident the AI should be in its picks

Slightly wilder or safer depending on range

🧠 Here’s a real example:

Prompt: “Give me three brand names for a personal finance app.”

Setting

Possible Output

Temp 0.2, Top-K 5

“Money Manager, Budget Pro, Finance Tracker”

Temp 0.8, Top-K 50

“WealthNest, CoinSage, BudgetBuddy”

Temp 0.8, Top-P 0.95

“NestEgg Now, SavvyStash, SpendSmart”

They’re all good—but as you move from lower to higher K or P, you start seeing names with more flair, creativity, or surprise.

3️⃣ Easy Prompting Techniques That Actually Work

  • 🛠️ The most powerful formats:

Technique

Example Prompt

Why It Works

Zero-shot

"Write a one-line caption for this photo."

Good for simple tasks but often too generic and lacks context.

One-shot / Few-shot

"Write a tweet like this: 'Mondays are for deep work and deeper coffee.'"

Shows the model a structure or style to follow, improving consistency.

Chain of Thought (CoT)

"I’m trying to calculate my budget. Let’s break it down step by step."

Helps with logical tasks by encouraging the model to reason through each step.

Step-back prompting

"Before writing the email, who is the audience and what do they care about?"

Adds useful context by zooming out before zooming in.

Role prompting

"You are a career coach. Help me write a confident LinkedIn summary."

Frames tone and expertise for more relevant, tailored output.

System prompting

"Summarize the text and return only the top 3 points in bullet form."

Defines how the model should behave or return results.

Contextual prompting

"Context: I’m writing a blog post for Gen Z freelancers. Suggest catchy titles."

Provides task-specific info that guides the model’s output effectively.

Self-consistency

Ask the same prompt multiple times: "Is this email spam or not? Explain why."

Improves reliability by comparing multiple reasoning paths and choosing the best.

Tree of Thoughts (ToT)

"Give me 3 ways to explain blockchain to a 10-year-old, then pick the best one."

Encourages deeper reasoning by exploring several paths before settling on one.

ReAct (Reason & Act)

"Use tools to find the weather in Paris this weekend, then suggest what to pack."

Combines reasoning and action for multi-step, research-based tasks.

Automatic Prompt Engineering

"Write 10 different ways someone might say: 'I need help resetting my password.'"

Uses the model to generate effective prompt variations and optimize instructions.

✅ Try this: Add “Let’s think step by step” to your next complex request and compare the results.

🎯 Alternatively, just use our tool Prompt Genie to create these super prompts for any AI task

👀 ICYMI

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Until next time,
Team DigitalSamaritan

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