Google's Prompt Engineering Guide Essentials

Google just released a detailed 68-page guide on prompt engineering - here’s what you need to know:

While aimed at their Vertex AI platform and a couple months old (a lifetime in AI!), its core principles for getting better results from LLMs (like Gemini, ChatGPT, Claude) remain quite helpful.

The Big Takeaway: Remember LLMs are powerful prediction engines, trained on vast amounts of human-generated text and data. They predict the next word based on the patterns they’ve learned.

Your prompt = The context guiding that prediction

Providing the right context – how you frame the request, the examples and data you include – dramatically impacts whether you get a generic response or truly useful output.

Based on the guide and experience, here are 3 foundational principles for better prompting:

(1) Show, Don’t Just Tell (Use Examples): Describing a task is good, showing is better. Providing clear examples (few-shot prompting) helps the AI grasp nuance, desired style, and specific formats, leading to more accurate results aligned with your needs.

(2) Be Clear & Specific: Ambiguity in = ambiguity out. Use concise language and be crystal clear about the desired output: What format? How long? What details must be included? Specificity avoids wasted time (and credits!) and gets you usable answers faster.

(3) Instruct Positively (Guide, Don’t Just Restrict): While rules (constraints) are sometimes needed, focusing on telling the AI what to do is generally more effective than listing what not to do. Positive instructions offer clearer direction and allow the AI more flexibility to generate creative yet relevant responses within those guidelines.

Here’s an example for a multifamily property management context - imagine asking about AI in property management:

Less Effective: “Tell me about AI benefits for property managers.” (Context is too broad, likely yields generic points).

More Effective: “Acting as a property manager, draft a brief explanation for a building owner highlighting how using AI predictive maintenance (analyzing attached HVAC sensor data) can cut emergency repair costs and boost tenant satisfaction. Provide 3 bullet points.” (Specific role, audience, action, data source, desired outcomes & format provide rich context).

The second prompt gives the AI a much clearer target, dramatically increasing the chance of getting a relevant, structured, and valuable response.

Constantly refining how we ask is essential to move beyond generic answers and unlock real strategic value from these tools.

What prompt tweaks have given you the biggest improvement in AI output quality? Share your wins!

Originally posted on LinkedIn. Join the discussion and share your thoughts there.