🔥5 High-impact techniques from Google’s playbook

🔥5 High-impact techniques from Google’s playbook

📌Chain of Thought (CoT) For tasks that involve reasoning, ask the model to “think step by step.” Prompt example: “When I was 3, my partner was 3x my age. I’m now 20. How old is my partner? Let’s think step by step.” ➡️Returns the right answer only when reasoning is made explicit.

📌Step-back prompting First ask a broader question to activate knowledge. Then use that context to get a sharper answer. Prompt example: Step 1: “What are 5 engaging video game settings?” Step 2: “Write a level storyline set in an underwater lab from the list above.” ➡️You can prime the model’s creativity with a general input—just like ideation in a real brainstorm.

📌Few-shot prompting Don’t just ask. Show + ask. Prompt example: "Parse this pizza order into JSON" + 2 worked examples ➡️What to remember: LLMs learn patterns, not rules. Examples matter.

📌Role prompting Give the model a job and a personality. Prompt example: “Act as a senior UX designer writing feedback for a junior intern, in a supportive tone.” ➡️Combine with temperature control (set between 0.8–1) for more expressive output.

📌Self-consistency sampling Prompt example: “Classify this email as IMPORTANT or NOT IMPORTANT. Let’s think step by step.” ➡️Warning: Higher cost, but much higher reliability in edge cases.

Tweak these for better AI output: Temperature Low (0.2) = factual High (0.8–1) = creative

Top-K Low (30) = focused High (40+) = variety

Top-P Low (0.9) = safe High (0.98–1) = freeform

💡Try: T=0.2, K=30, P=0.95 for clean results. Go higher for creative writing or wild ideas.

What if your AI isn't underperforming—you're just prompting it wrong?

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