Show Notes
Learn how to turn prompting into a repeatable, high-quality process by using a meta prompt—prompting the prompt itself. No PhD required; you can scale your outputs by building and reusing meta prompts.
Meta Prompt 101: what it is and why it matters
- A meta prompt is a prompt that instructs the model to generate prompts for other tasks.
- Benefits:
- Reduces the mental load of prompt-writing
- Improves output quality by standardizing structure and details
- Lets you chain prompts for complex tasks without starting from scratch
- The concept is framed like Inception: you tell the model to create a prompt, then you use that generated prompt to drive the actual task.
Building a meta prompt: step-by-step
- Start with a strong opening: you are an expert prompt writer. Do not omit any detail.
- Include a user input placeholder, e.g., [USER_INPUT], so the generated prompt can adapt to new tasks.
- Define what the meta prompt should specify:
- Persona or tone
- Details to include
- Desired output length
- Delimiters for structured output
- Use delimiters (XML-like) to make the output easy to parse and reuse.
- Provide examples to guide the model and set expectations.
- Save the resulting prompt so you can reuse it for similar tasks.
Code block: a simplified meta-prompt skeleton
text
You are an expert prompt writer. Do not omit any detail.
You will generate a new prompt for a given task. The output prompt MUST include:
- USER_INPUT placeholder for the actual user content
- Persona: [describe tone and style]
- Details: [list required specifics]
- Length: [short/medium/long]
- Delimiters: [XML-like markers or other clear boundaries]
- Examples: [provide a couple of mini examples]
Example workflow: generating click-worthy YouTube titles
- Use a meta prompt to craft a specialized prompt for title generation.
- Include:
- USER_INPUT for the video topic
- Persona: punchy, attention-grabbing, concise
- Length: specify desired title length
- Delimiters to structure the output
- Examples to illustrate expected format
- Process:
- Paste the meta prompt into your tool
- Insert your user input (video topic)
- Retrieve the generated prompt and use it to produce titles
- Benefit: you consistently get well-structured, tailored prompts without reinventing the wheel each time.
Reuse and saving prompts: library and school
- Save your generated prompts so you don’t rewrite them every time.
- Access the prompt library under the platform’s “School” features:
- Prompt Library: a collection of prompts created with meta prompts
- Classroom: a space to explore and organize prompts
- A practical example: prompts built for image generation can be generated with the same meta-prompt approach. For instance, prompts that describe or guide image generation (e.g., perplexity-style images) can be produced automatically and then refined.
Practical demos and tips
- Meta prompts can generate prompts for non-text tasks as well (e.g., image generation prompts, image descriptions).
- You can create a few high-leverage meta prompts and reuse them across many tasks.
- Start with complex tasks and gradually build simpler variants to cover common needs.
Tips for reliable prompts
- Put a clear user input placeholder to keep inputs dynamic.
- Specify persona and tone to control voice and style.
- Use delimiters to make outputs parseable and reusable.
- Include a few concrete examples to anchor the model.
- Save and share prompts in a centralized library to accelerate future work.
Quick takeaways
- A meta prompt lets you write prompts about prompts, turning prompting into a repeatable workflow.
- You don’t need a PhD in prompt engineering—build a few strong meta prompts, then reuse them.
- Use placeholders, clear delimiters, and persona settings to get consistent results.
- Explore the prompt library and School features to discover and share effective prompts.