Paraprompt is an open prompt registry — a community-curated library of AI prompts, prompt techniques and prompt templates for every major model (ChatGPT, Claude, Gemini, Deepseek, Llama, Mistral, and image/video/audio models). Browse, fork, share, and run prompts directly from the hub or via our Chrome extension.

What is a prompt registry?

A prompt registry is to AI prompts what npm is to JavaScript packages or GitHub is to source code: a central, searchable, versioned home where prompts are discovered, shared, attributed and improved by a community. Paraprompt is the open prompt registry for the LLM era.

Which AI models does Paraprompt support?

Paraprompt is model-agnostic. Prompts are tagged with the models they were designed for — including ChatGPT/GPT-4o, Claude (Opus, Sonnet, Haiku), Gemini Pro, Deepseek, Llama 3, Mistral/Mixtral, Qwen, Grok, Perplexity, and image/video models like Midjourney, Stable Diffusion, DALL·E, Sora and Runway.

What prompt-engineering techniques are covered?

Every prompt is tagged with one of five canonical techniques: Basic (zero-shot), Few-Shot, Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Retrieval-Augmented Generation (RAG). Each technique has a dedicated guide on Paraprompt explaining when and how to use it.

Yes. Browsing, searching, downloading and sharing prompts is free. Account creation is free. The browser extension is free.

How is Paraprompt different from PromptBase or FlowGPT?

Paraprompt is open and community-first: prompts are tagged by technique, ranked by community signals (likes, power, funny), grouped into collections, and accessible from anywhere via our extension. There is no marketplace gate — every prompt is free to use and remix.

Can I save my own prompts to Paraprompt?

Yes — sign in, create prompts in the hub, organise them into collections, and access them from any browser tab via the Paraprompt Chrome extension. You decide whether each prompt is private or public.

What is the Paraprompt Chrome extension?

The Paraprompt Chrome extension is a side panel that surfaces your prompts on every page — including ChatGPT, Claude.ai, Gemini and any text input. Trigger any prompt by shortcut, fill in template variables on the fly, and paste straight into the model's UI.

What's the difference between zero-shot, few-shot, and chain-of-thought prompting?

Zero-shot gives the model a direct instruction with no examples. Few-shot provides 2–8 worked examples to anchor format and tone. Chain-of-Thought adds an explicit "think step by step" trigger so the model reasons before answering — best for math, logic and multi-step problems.

When should I use RAG instead of a longer prompt?

Use RAG (Retrieval-Augmented Generation) any time the answer depends on data the model wasn't trained on — your docs, tickets, code, or anything that changes after the model's cutoff. Pair RAG with explicit grounding instructions and citations for the highest factual accuracy.

Does Paraprompt support image, video and audio prompts?

Yes. Prompts on the hub support text, image, audio and video previews — so a Midjourney prompt shows the rendered image, a Suno prompt plays the generated audio, and a Sora prompt embeds the video output.

Can I use Paraprompt prompts commercially?

Most prompts on Paraprompt are shared by their authors for free reuse. Each prompt page shows attribution; check the author's note for any specific terms.

Prompt technique

Chain-of-Thought (CoT) Prompting

Unlock multi-step reasoning by asking the model to think before it answers.

What it is

Chain-of-Thought prompting elicits intermediate reasoning steps from the model before the final answer. Introduced by Wei et al. (2022), CoT dramatically improves performance on math, logic, multi-hop QA and planning tasks. The trigger can be as simple as appending "Let's think step by step" or providing few-shot examples that include explicit reasoning traces.

When to use it

✓Math, logic, code reasoning, or any multi-step decision
✓Tasks where the wrong answer is also a plausible answer
✓Whenever you'd want a junior teammate to "show their work"

Example

Q: A bakery sells 240 croissants over 3 days. Tuesday they sold twice as many as Monday, and Wednesday they sold 30 more than Tuesday. How many did they sell on Monday?

Let's think step by step.

Why it works: The trigger phrase forces the model to enumerate constraints and solve them in order, surfacing arithmetic errors before the final answer.

Pitfalls

!Long reasoning chains burn tokens — for production, ask the model to think internally then return only the final answer.
!CoT can hallucinate confidently — verify with self-consistency or an external check on critical paths.

Pairs well with

Few-Shot →Tree-of-Thought →