ChatGPT vs Claude vs Perplexity vs Grok — Perplexity
The same task often needs a slightly different prompt style per model.
The prompt
What are the three most significant changes to [topic] in the last 6 months? Cite sources for each claim and note the publish date of each source.
What to replace
Swap these placeholders for your own details before running the prompt:
[topic]your own value
Pro tip: Use Perplexity when you need sourced, current facts. Use ChatGPT/Claude for structured writing and reasoning. Use Grok for fast, casual takes and trend awareness.
How to use this prompt
- Copy the prompt using the button above.
- Replace [topic] with your own details — the more specific you are, the better the output.
- Paste it into Perplexity and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
Learn the technique
ChatGPT vs Claude vs Perplexity vs Grok
Module 3 — Prompting Across Different AI Models · Prompt Engineering Fundamentals
Related prompts
The 5-Part Prompt Framework — ChatGPT
Every strong prompt combines five parts: Role (who the AI should act as), Context (background info it needs), Task (the specific ask), Format (how you want the output structured), and Constraints (length, tone, things to avoid).
ClaudeThe 5-Part Prompt Framework — Claude
Every strong prompt combines five parts: Role (who the AI should act as), Context (background info it needs), Task (the specific ask), Format (how you want the output structured), and Constraints (length, tone, things to avoid).
ChatGPTZero-Shot vs Few-Shot Prompting — ChatGPT
Zero-shot means asking directly with no examples.
GrokZero-Shot vs Few-Shot Prompting — Grok
Zero-shot means asking directly with no examples.
ChatGPTChain-of-Thought Prompting — ChatGPT
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
ClaudeChain-of-Thought Prompting — Claude
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..