ChatGPT’s Em Dash Fix: A Win for Personalization in AI

The faint glow of my laptop screen illuminated a late-night battle.

Not against a game boss, but against a stubbornly verbose artificial intelligence.

ChatGPT, my supposed creative partner, had once again peppered my draft with an enthusiastic—if entirely inappropriate—abundance of em dashes.

Each dash felt like a little digital sigh, disrupting the rhythm, breaking the flow, and driving my inner editor to distraction.

I had tried everything: polite requests, firm commands, even pleading in my prompts.

Nothing worked.

Then, a notification popped up—a post from Sam Altman on X, announcing a development he described as a small-but-happy win.

ChatGPT could finally be instructed to stop using em dashes in custom instructions.

In that moment of relief, a deeper truth dawned on me: the seemingly trivial act of controlling punctuation was, in fact, a profound step in our evolving relationship with AI.

It hinted at a future where our digital companions truly understood and adapted to our unique human needs.

In short: OpenAIs ChatGPT now responds to custom instructions to avoid em dashes, a small-but-happy win for personalization and instruction-following in GPT-5.1.

This development highlights ongoing challenges in controlling LLM outputs and the black box nature of AI.

Why This Matters Now: Beyond the Code, Towards Human Control

We are in an era of unprecedented AI advancement, where large language models (LLMs) are reshaping everything from content creation to customer service.

Yet, for all their dazzling capabilities, these models often present a paradox: immense power coupled with surprising stubbornness on seemingly minor details.

The saga of ChatGPT and the em dash is a microcosm of this larger reality.

This isnt merely a grammarians grievance; it speaks to the fundamental challenge of achieving precise control over artificial intelligence and bending its powerful algorithms to our human will.

OpenAI, the architect behind ChatGPT, has made no secret of its ambitious long-term goals: superintelligence, artificial general intelligence (AGI), and even autonomous AI researchers.

Yet, the very fact that a fix for a punctuation quirk is hailed as a significant victory underscores the complex, often unpredictable nature of AI development.

It highlights that while the grand visions may still be distant, the immediate frontier is about refining usability, enhancing personalization, and giving users more granular control over these powerful, yet sometimes idiosyncratic, digital minds.

The Problem with Punctuation: Unmasking the Black Box

The core challenge with ChatGPTs former overuse of the em dash was never truly about punctuation itself.

It was about control, precision, and the often-frustrating black box nature of large language models.

Imagine asking a brilliant but overly zealous assistant to write a report, and despite repeated requests, they insist on using an obscure, repetitive stylistic flourish.

The problem isnt their intelligence; its their inability to truly understand and consistently adhere to your specific, nuanced instructions.

This issue of stylistic control reveals a deeper truth about LLMs: their outputs are the result of incredibly complex statistical patterns derived from vast datasets.

They dont inherently understand grammar rules or stylistic preferences in a human sense; they predict the most probable next token.

This means that a seemingly simple request, such as to exclude em dashes, can be surprisingly difficult for the model to interpret and apply consistently across all contexts.

The challenge highlights the difference between an AI that can generate text and one that can truly internalize and follow a complex set of instructions for nuanced stylistic compliance, a hurdle that remains significant for large language models.

OpenAIs Strategic Pivot: From AGI Aspirations to Practical Personalization

The recent developments at OpenAI—specifically, the ability to control em dash usage—reveal a strategic pivot in the companys focus.

While the aspirational goals of superintelligence and AGI remain on the distant horizon, the immediate emphasis is clearly on practical usability and user empowerment through enhanced personalization.

The latest model, GPT-5.1, explicitly prioritizes improved instruction following and more robust personalization features.

This is reflected in the em dash clampdown: rather than a fundamental rewrite of the models core writing style, it represents a more effective weighting of custom user instructions.

This allows users to tailor ChatGPTs output more effectively, leading to more customized and potentially higher-quality content that aligns with specific stylistic preferences.

This emphasis on practical usability for current-generation AI suggests that as foundational AGI breakthroughs remain distant, the industry will focus more on making existing AI tools more adaptable and responsive to individual user needs.

The fact that this fix is a user-by-user customization, rather than a universal default change, also provides a candid glimpse into the continuing complexities of large language models.

Despite the advances, some users in responses to Altmans post on X have indicated that their version of ChatGPT continues to generate em dashes, even after being given the instruction.

This illustrates that developing universal, scalable solutions for subtle stylistic issues in LLMs remains a significant challenge.

OpenAIs presentation of personalization as a solve suggests that finding a solution at scale, one that fundamentally alters the models default behavior across all instances, remains extremely challenging.

Your Playbook for Harnessing Controllable AI Today

For marketing, business, and AI professionals, these insights from OpenAI offer a crucial playbook for maximizing the utility of LLMs while navigating their inherent complexities.

The shift towards greater personalization means your ability to articulate and implement custom instructions will be a key differentiator.

• Master Custom Instructions: View custom instructions as a powerful lever for controlling AI output.

  Beyond simple commands, learn to craft detailed style guides and preference settings within your AI tools.

  This is directly tied to the improved personalization in GPT-5.1, allowing you to tailor content more effectively.

• Embrace Iterative Refinement: Recognize that achieving perfect AI output is an ongoing process, not a one-time fix.

  Be prepared to provide continuous feedback and adjust your custom instructions.

  This acknowledges the black box nature of LLMs and the need for user-driven refinement.

• Prioritize Human Oversight: Even with enhanced instruction following, human oversight remains critical.

  AI is a powerful assistant, not a replacement for nuanced judgment.

  This ensures that the content aligns not only with stylistic preferences but also with brand voice, ethical standards, and strategic objectives.

• Understand AIs Limitations: Acknowledge that while AI can mimic human writing, its comprehension of subtle stylistic nuances is statistical, not intuitive.

  This helps manage expectations and guides your approach to prompt engineering.

  The challenge of the em dash fix at scale underscores that universal stylistic adherence is still difficult.

• Leverage Personalization for Brand Consistency: For businesses, consistent brand voice is paramount.

  Utilize personalization features to ensure AI-generated content adheres to your specific style guides, tone, and formatting requirements across all outputs.

  This enhances brand perception and reduces the need for extensive human editing.

Navigating the AI Frontier: Risks, Trade-offs, and Ethics

The incremental progress seen with features like the em dash control brings important ethical considerations and risks to the forefront.

While seemingly minor, the ability to exert precise control over AI output can have broader implications.

One significant risk is the potential for unintended bias to be reinforced through personalization.

If users or organizations consistently instruct AI to produce content that aligns with existing biases, these systems could inadvertently become echo chambers, limiting diverse perspectives.

Another trade-off lies in resource allocation: investing heavily in fine-tuning for minute stylistic control might divert resources from tackling more pressing issues like factual accuracy or reducing hallucinations in LLMs.

Ethically, theres a delicate balance between user empowerment and developer responsibility.

While users gaining control over specific outputs is positive, the underlying black box nature of LLMs means developers still bear the responsibility of ensuring the base model is robust, fair, and safe by default.

If the root cause of issues like the em dash overuse is not understood or fixed at scale, it could imply a deeper, more unpredictable behavior within the model that personalization merely sidesteps.

• Mitigation strategies involve transparent reporting from AI developers about model behavior and limitations, robust internal ethical guidelines for personalization use, and encouraging users to apply diverse custom instructions.

  For AI consultants and businesses, this means continuously evaluating the ethical implications of their personalized AI deployments and advocating for stronger default model behaviors.

Measuring Success: Tools, Metrics, and Cadence

To effectively navigate this evolving landscape, organizations need a clear framework for measuring the impact of personalization and instruction following in their AI deployments.

This is about ensuring AI is not just productive, but precisely aligned with strategic goals.

Tools and Frameworks:

Utilize internal content analysis tools to check for adherence to style guides.

Employ prompt engineering best practices that incorporate clear, layered instructions.

Monitor user feedback channels for specific stylistic issues.

The continuous evolution of AI platforms will likely introduce more sophisticated tools for controlling output at a granular level.

Key Performance Indicators (KPIs):

• Track Adherence to Custom Instructions: Measure the percentage of AI-generated content that successfully follows specified stylistic or formatting guidelines.
• Monitor Content Editing Time Reduction: Quantify the time saved by human editors due to improved AI output quality after applying personalization.
• Assess User Satisfaction Scores: Gather feedback from users on how well AI output meets their specific needs and preferences.
• Evaluate Consistency Across Outputs: Measure stylistic and factual consistency across a range of AI-generated content within the organization.
• Monitor AI Prompt Efficiency: Analyze how fewer or shorter custom instructions can achieve desired results over time.

Review Cadence:

Conduct weekly quality checks on AI-generated content against custom instructions.

Hold monthly feedback sessions with content teams and AI users to identify emerging stylistic patterns or areas for improved personalization.

Quarterly, perform a comprehensive audit of AI outputs to ensure alignment with broader brand and ethical guidelines, adapting custom instruction strategies as needed.

FAQ

• Q1: Has OpenAI achieved superintelligence or AGI? No, OpenAI has not achieved superintelligence or artificial general intelligence (AGI), nor its planned autonomous AI researcher, as stated in the article.
• Q2: How did ChatGPT stop misusing the em dash? Users can now tell ChatGPT not to use em dashes in their custom instructions within the chatbots personalization settings.

  This is a user-by-user change, not a default model fix.

• Q3: What is GPT-5.1? GPT-5.1 is the latest model from OpenAI, which features improved instruction following and more personalization capabilities, with the em dash control being one example of this compliance.
• Q4: Why is the em dash fix a small-but-happy win? CEO Sam Altman described it as a small-but-happy win because it demonstrates the models improved ability to follow specific user instructions, even if its a minor stylistic detail (Sam Altman).
• Q5: Does this fix mean OpenAI understands why ChatGPT misused the em dash? The article suggests that OpenAI has figured out how to weight custom instructions more heavily, but it still seems like the company cant figure out why the problem happened in the first place or persists.

Conclusion

The journey to command ChatGPT to stop misusing the em dash is more than a tale of punctuation; it is a profound narrative about our evolving relationship with artificial intelligence.

It highlights that while the grand promises of AGI captivate the imagination, true progress often lies in the iterative, human-centric work of making AI more compliant, personal, and genuinely useful in our everyday tasks.

OpenAIs small-but-happy win signals a future where user empowerment, through precise custom instructions, becomes as vital as raw computational power.

For businesses and individuals, this means embracing AI not as a perfect oracle, but as a powerful, adaptable partner that responds best to clear, thoughtful guidance.

The future of AI is not just about what the models can do, but how effectively we can tell them what we need.

Let us continue to refine our instructions, push for transparency, and shape these digital minds to reflect our human intent, one perfectly punctuated sentence at a time.

Glossary

• Artificial General Intelligence (AGI): Hypothetical AI with human-like cognitive abilities, capable of understanding, learning, and applying intelligence across a wide range of tasks.
• Custom Instructions: User-defined settings within AI models like ChatGPT that provide persistent guidance on how the AI should respond to prompts.
• Large Language Model (LLM): An AI model trained on vast amounts of text data, capable of understanding, generating, and translating human-like text.
• Personalization Features: Capabilities within an AI system that allow users to tailor its behavior, style, or output to their specific preferences.
• Prompt Engineering: The art and science of crafting effective inputs (prompts) to AI models to achieve desired outputs.
• Black Box Nature of LLMs: A term referring to the difficulty in understanding the internal workings or decision-making processes of complex AI models.
• Stylistic Control: The ability to dictate the writing style, tone, and formatting of AI-generated text.

References

• ChatGPT Achieves a New Level of Intelligence: Not Using the Em Dash
• Sam Altman, X post
• OpenAI, Threads post