Google Releases 10-Step Guide to Prompt Engineering

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Google Releases 10-Step Guide to Prompt Engineering
Google Releases 10-Step Guide to Prompt Engineering

Google Releases 10-Step Guide to Prompt Engineering

Introduction: The Rise of Prompt Engineering

The world of large language models (LLMs) moves fast. More people are turning to Gemini, GPT, Claude, and open-source models for tasks that used to belong only to coders and machine learning experts. Whether you’re handling customer queries, building chatbots, generating code, or parsing mountains of text, you’ve probably noticed: the prompt you give a model is everything.

What was once a hidden craft—prompt engineering—now sits front and center. Google knows this, and their release of a detailed 68-page prompt engineering whitepaper (PDF) proves it’s not just for developers anymore. This guide, packed with advice, real-world examples, and hard-won lessons, makes prompt design approachable for anyone using the API, from analysts to marketers.

Now, let’s break down Google’s 10-step guide and see why prompt engineering is becoming a must-have skill—no matter your background.

Getting Started: What Is Prompt Engineering?

Prompt engineering boils down to one big idea: the words you use to “talk” to a model shape everything it does. You don’t need to be a machine learning wizard. If you can write instructions, you’re already halfway there.

  • Text prompts are like the steering wheel of a language model. They point it toward the answer you want, whether that’s a summary, a list, a joke, or a block of code.
  • The design of your prompt (the words, the structure, even the examples you include) changes what the model “thinks” you want.
  • Messy, vague, or confusing prompts mean messy, vague, or confusing answers.

For anyone new to this: think of prompt engineering as the skill of asking just the right question in just the right way. That’s how you get models to work for you, not against you.

Choosing Your Model and Configuration

Before diving into prompt design, you need to pick the right model and set it up for your task.

Model Choices

  • Gemini: Google’s most recent line, known for flexibility and API integration.
  • GPT (OpenAI): Popular for general tasks, creative writing, and coding help.
  • Claude (Anthropic): Often favored for longer conversation and context retention.
  • Open-source models (Gemma, LLaMA, etc.): Great for privacy, customization, or when you want to avoid vendor lock-in.

Different models will treat your prompt differently. You might get a sharp, concise list from one and a rambling essay from another—even if you ask the same question.

Configuration Knobs

Every major LLM lets you adjust how it responds:

  • Output Length: Controls how much the model says. Too short, and you risk missing details. Too long, and you might get rambling or filler.
  • Temperature: Sets how “creative” or “random” the model gets.
  • Top-K: Limits choices to the K most likely next words.
  • Top-P (Nucleus Sampling): Limits choices to the smallest pool of words whose probabilities add up to P.

Tweak these settings to match your task. Want strict, predictable answers? Go low on temperature. Need more brainstorming or variety? Turn it up.

Decoding Output Settings: Temperature, Top-P, and Top-K

If you’ve ever gotten weird, repetitive, or just plain off-the-wall answers from a model, your output settings might be the cause.

What Do These Settings Actually Do?

  • Temperature: Low values (like 0.1) make the model pick its “best guess” every time—good for math or code. High values (like 0.8 or 1.0) let it take more risks—great for creative writing.
  • Top-K: By restricting the model to the K most likely choices, you tighten or loosen its creativity.
  • Top-P: Lets the model sample only from a pool of words that add up to a set probability. Combine with temperature for fine control.

Common Pitfalls

  • Repetition loops: The model gets stuck saying the same thing over and over. Usually caused by bad combos of temperature and top-K/top-P.
  • Hallucinations: The model invents facts or goes off-script. Sometimes, higher temperature or too much freedom causes this.
  • Abrupt cutoffs: If you set the output length too short, you might get unfinished sentences or broken JSON.

Finding the Sweet Spot

  • For factual and deterministic answers (like code or math), try temperature 0–0.2, top-p around 0.9, top-k around 20–30.
  • For creative or brainstorming tasks, experiment with temperature 0.7–1.0, top-p 0.95–0.99, top-k 40 or higher.

Testing is your friend. Every task is a bit different.

Prompting Techniques: Moving Beyond Simple Instructions

Prompt engineering is more than just typing a question. The best results come from thinking about how you ask.

Zero-Shot Prompting

  • Just ask. No examples, no context.
  • Great when the model already “knows” the task.
  • Example: Summarize this article in one paragraph.

One-Shot and Few-Shot Prompting

  • Show the model a single example (one-shot) or a handful (few-shot).
  • Helps for tasks where you want a specific style or format.
  • Example: Example: Review: "I loved the movie." Sentiment: POSITIVE Review: "It was okay." Sentiment:

System Prompts

  • These set the overall “rules of the road.”
  • Example: Only respond with JSON. Do not output any other text.

Role Prompts

  • Assign the model a persona or point of view.
  • Example: Act as a travel guide. Suggest three museums in Paris.

Context Prompts

  • Feed the model relevant background.
  • Example: Context: You are writing for a blog about retro arcade games. Suggest three article topics.

Mix and match these for more control.

Advanced Prompting: Thinking in Chains, Steps, and Trees

When you want more than just a quick answer—when you need the model to reason, explain, or solve multi-step problems—advanced techniques come into play.

Chain-of-Thought

  • Ask the model to “think out loud” and show each step.
  • Example: When I was 3, my brother was twice my age. Now I am 20. How old is my brother? Let’s think step by step.
  • This approach helps with math, logic, and complex reasoning.

Step-Back Prompting

  • Have the model answer a more general question first, then use that answer to solve the specific problem.
  • Good for creative tasks or when you want broader context.

Self-Consistency

  • Run the same prompt several times with different randomness, then pick the most common answer.
  • Helps reduce flukes and find reliable responses.

Tree-of-Thoughts

  • Ask the model to follow multiple reasoning paths at once, not just a single chain.
  • Useful for brainstorming, planning, or creative problem-solving.

ReAct (Reason + Act)

  • The model reasons, then takes an action (like running a search), then reasons again.
  • Often used with external tools or APIs for more interactive tasks.

These methods open the door to deeper problem-solving and more nuanced answers.

Writing for Structure: Getting JSON and Other Formats

LLMs aren’t just for chat—they can output structured data ready for your apps and tools.

Why Structure Matters

  • Clean, machine-readable formats (like JSON) are gold for anyone who needs to automate downstream tasks or feed results into other systems.

How to Prompt for Structure

  • Be clear: Extract the following details and output as valid JSON: Name, Date, Email.
  • For more control, provide a schema: Return the output using this schema: { "product": string, "price": number, "release_date": string (format: YYYY-MM-DD) }

Dealing with Broken Outputs

  • Sometimes the model cuts off or forgets a bracket.
  • Tools like json-repair can patch up broken JSON.
  • Keep output length and formatting in mind to avoid truncation.

Working with Schemas

  • Give the model both a schema and the data you want to fill—this guides the output and keeps things tidy.

Prompting for Code: From Bash to Python and Beyond

Language models aren’t just for words—they’re surprisingly handy for writing and explaining code, too.

Generating Code Snippets

  • Ask for scripts, functions, or full programs.
  • Example: Write a Bash script that renames every file in a folder by adding the prefix "draft_".

Explaining and Debugging Code

  • Feed the model a chunk of code and ask for an explanation or bug fix.
  • Example: Explain what this Python code does. Debug this code and suggest improvements.

Translating Between Languages

  • Convert code from one language to another.
  • Example: Translate this Bash script to Python.

Reviewing and Improving Code

  • Ask the model to review for bugs, suggest optimizations, or rewrite for clarity.

Prompting for code can supercharge your workflow, even if you’re not a full-time developer.

Experimentation: Tinkering, Testing, and Documenting

No one gets the perfect prompt on the first try. The real magic comes from testing, tweaking, and keeping track.

The Loop

  1. Write a prompt.
  2. Run it and check the output.
  3. Adjust wording, settings, or examples.
  4. Repeat until you get what you want.

Documenting

  • Keep track of each prompt, its settings, and what it produced.
  • Store them in a spreadsheet or prompt library.
  • Save links to working prompts (especially if you’re using tools like Vertex AI Studio).

Why It Matters

  • You’ll want to revisit old prompts, compare results, and see what works best.
  • Documentation saves time and helps you spot patterns.

Habits for Effective Prompting

The best prompt engineers share a few habits that make their work stand out.

Use Clear Instructions

  • Be direct and specific.
  • Example: Summarize this article in three bullet points, each no longer than 20 words.

Give Well-Chosen Examples

  • Use relevant, high-quality examples for few-shot prompts.
  • Mix up the order in classification tasks to avoid bias.

Get Specific

  • Spell out the format, style, and details you want.
  • Avoid vague instructions.

Play with Variables and Styles

  • Use variables to make prompts reusable.
  • Try different question formats, tones, and output structures.

Collaborate

  • Share prompts with other engineers.
  • Compare notes and see who gets the best results.

Wrapping Up: Becoming Fluent in Prompt Engineering

Mastering prompt engineering means you can shape language models to fit your exact needs—whether that’s generating code, extracting data, writing blog posts, or building smarter bots.

Here’s what changes when you get fluent:

  • You waste less time wrestling with bad outputs.
  • You build a toolkit of prompts and strategies that you can use, adapt, and share.
  • You solve problems faster, with better results.

Resources for Growing Your Skills

Prompt engineering is now a practical, learnable skill for anyone working with language models. With the right habits, a bit of curiosity, and the resources Google has shared, you can go from guessing to guiding—even if you don’t write code for a living.

Ready to transform how you work with language models? Download the full Google Prompt Engineering Guide (PDF) and start building your own prompt toolkit today.

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