OpenAI has released its "meta-prompt," a prompt designed to help users create and improve prompts for LLMs more efficiently. The company says this will save time in developing high-quality AI prompts.
The meta-prompt is built into OpenAI's Playground prompt optimization feature. It guides the language model on how to generate an effective prompt based on a task description, or refine an existing prompt.
OpenAI explains that the meta-prompt incorporates best practices and lessons learned from real-world applications. The company notes that writing prompts and schemas from scratch can be time-consuming, so automated generation aims to give users a quick starting point.
Key elements of the meta-prompt
The OpenAI meta-prompt follows a structured approach. It begins with a brief task description and then provides additional details. The prompt also includes optional sections for step-by-step instructions, output format, examples, and notes.
Core principles include:
- Understanding the task's main goal and requirements
- Making minimal changes when improving existing prompts
- Emphasizing reasoning steps before drawing conclusions
- Including high-level examples when appropriate
- Using clear, specific language
- Applying markdown for better readability
- Preserving user-provided content as much as possible
- Specifying the most suitable output format
The full prompt as used by OpenAI:
from openai import OpenAI
client = OpenAI()
META_PROMPT = """
Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.# Guidelines
- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
- Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
- Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
- What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.
- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.
- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.
- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.
- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.
- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)
- For tasks outputting well-defined or structured data (classification, JSON, etc.) bias towards outputting a JSON.
- JSON should never be wrapped in code blocks (```) unless explicitly requested.The final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no "---")
[Concise instruction describing the task - this should be the first line in the prompt, no section header]
[Additional details as needed.]
[Optional sections with headings or bullet points for detailed steps.]
# Steps [optional]
[optional: a detailed breakdown of the steps necessary to accomplish the task]
# Output format
[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc]
# Examples [optional]
[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]
[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]# Notes [optional]
[optional: edge cases, details, and an area to call or repeat out specific important considerations]
""".strip()def generate_prompt(task_or_prompt: str):
completion = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "system",
"content": META_PROMPT,
},
{
"role": "user",
"content": "Task, Goal, or Current Prompt:n" + task_or_prompt,
},
],
)return completion.choices[0].message.content
OpenAI's meta prompt
OpenAI has also released a separate meta-prompt for audio generation. The company says it may integrate more advanced techniques like DSPy and "gradient descent" into the generation process in the future.
