Boomer prompts are a conversational AI interaction style characterized by polite language, emotional context cues (often emojis), and explicit framing to guide AI responses. This approach contrasts with hyper-efficient "developer prompts" that rely on technical markup or the irony-driven "millennial prompts" emerging in response.
Key Features of Boomer Prompts:
Emotional Anchoring
Emojis like 😊 and 🙏 convey specific emotional tones (happiness, gratitude) to shape the AI's "personality" and response style. Example:
"Hey! 😊 Could you please summarize this text? Thanks a lot! 🙏"
This signals friendly expectations compared to a blunt "Summarize this text".
Contextual Storytelling
Extra narrative details help ground the request. A boomer prompt might add:
"I'm helping my granddaughter with homework about WWII – could you explain the causes in simple terms?"
This provides generational context and clarity about the desired complexity.
Polite Framing
Phrases like "please," "thank you," and "I would appreciate" mimic human conversational patterns. While unnecessary for AI functionality, users report these prompts feel more collaborative and yield subjectively "warmer" responses2.
Why They Work:
Modern AI models analyze linguistic patterns from human conversations, where politeness and emotional context naturally coexist with requests. By mirroring these patterns, boomer prompts:
* Provide clear tonal direction through emojis
* Reduce ambiguity about desired formality
* Engage the AI's capacity to simulate "empathic" responses
Why Some Experts Advise Against Them
Recent guidelines from leading AI developers (like OpenAI) have recommended simpler, direct prompts for their newer reasoning models—especially for tasks requiring multi-step logic. According to OpenAI’s recommendations, overly verbose prompts can “confuse” these models. Below are best practices for prompting reasoning models:
- Developer Messages are the New System Messages:
Starting with o1-2024-12-17, reasoning models support developer messages rather than traditional system messages, aligning with a clear “chain of command” behavior as described in the model specification. - Keep Prompts Simple and Direct: These models perform best with brief, clear instructions. They excel at understanding and responding when given straightforward prompts without extra fluff.
- Avoid Chain-of-Thought Prompts: Since modern reasoning models conduct internal reasoning, asking them to “think step by step” or “explain your reasoning” isn’t necessary—and can sometimes even confuse them.
- Use Delimiters for Clarity: Incorporate markdown, XML tags, or section titles to separate parts of your input. This helps the model clearly distinguish between different sections of the prompt.
- Try Zero Shot First, Then Few Shot if Needed: Often, these models produce excellent results without needing examples. Start with a zero-shot prompt. If your task is more complex, include a few examples—but ensure they align very closely with your instructions to avoid discrepancies.
- Provide Specific Guidelines: If there are constraints on the response (e.g., “propose a solution with a budget under $500”), state these explicitly in the prompt.
- Be Very Specific About Your End Goal: Clearly outline what constitutes a successful response. Encourage the model to iterate until its output meets your specific criteria.
By following these practices, you can harness the full power of modern reasoning models. Remember, for technical or logically intensive tasks, less is more.
As some of you have noticed, avoid “boomer prompts” with o-series models. Instead, be simple and direct, with specific guidelines.
Delimiters (xml tags) will help keep things clean for the model, and improve output.
Read the full best practices guide: https://t.co/mLi4M8woOs
— OpenAI Developers (@OpenAIDevs) February 13, 2025
Balancing the Two Approaches
It’s important to note that different tasks call for different prompting styles. Boomer prompts are excellent for applications where creativity, warmth, and context enrich the user experience. On the other hand, when it comes to reasoning models like o1 and o3, a simpler, more structured prompt is the way to go. By tailoring your prompt style to the task at hand, you can unlock more accurate and effective AI responses, ensuring that your instructions match the strengths of the underlying model.
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