Is it possible to create a model agnostic prompt?

Key Concepts

• Model Agnostic Prompting
• Agent Scaling
• Capacity Limits
• Prompt Optimization
• Evaluation Metrics
• Model Family Differences (GPT-5, Sonnet, Claude)
• XML Text Preference

Model Agnostic Prompting and Agent Scaling

The core issue discussed is the feasibility and value of creating "model agnostic" prompts, particularly in the context of scaling AI agents to production. A significant challenge identified is the "capacity limits" of individual models. The ability to seamlessly swap between different model families, such as from GPT-5 to Sonnet, is presented as a potential solution for scaling. However, a major hurdle is that prompts optimized for one model family often perform poorly with others.

The Problem of Prompt Optimization Across Model Families

The transcript highlights that a prompt meticulously crafted and optimized for a specific model family (e.g., GPT-5) tends to perform "terribly" when used with a different family (e.g., Sonnet). This lack of portability necessitates re-optimization for each new model or model family, which is a time-consuming and iterative process. The speaker acknowledges the "pain of like creating a good prompt model update model changes and you need to iterate on it."

The Proposed Solution: Automated Prompt Optimization

Instead of aiming for a single "model agnostic prompt," the recommended approach is to establish a "process which allows me to optimize my prompts automatically for different models and use cases." This shifts the focus from creating a universal prompt to building a system that can adapt and optimize prompts dynamically.

The Role of Evaluation in Prompt Optimization

The success of automated prompt optimization is fundamentally tied to "evaluations." Without robust evaluation metrics, it's impossible to determine if a prompt is performing well for a given model and task. The speaker emphasizes that "all goes again back to like evaluations."

Understanding Model-Specific Prompting Behaviors

The transcript provides specific examples of how different models exhibit distinct preferences and performance characteristics:

• GPT-5 to Sonnet Transition: It's stated that a GPT-5 optimized prompt will "work differently on set 100%." This implies a significant performance drop or altered behavior when migrating to a different model.
• Claude's XML Text Preference: The speaker notes that "Claude is very favorable of XML text." This suggests that structuring prompts using XML can lead to better results with Claude models.
• GPT-5 and XML: While not as definitive as with Claude, the assumption is made that GPT-5 would still perform "very good with those [XML text]," but potentially "not as good" as Claude. This indicates that while some general principles might apply, specific model architectures and training data influence optimal prompt formatting.

Anticipating Convergence in Prompting Standards

The question is posed whether the speaker anticipates any "convergence in prompting standards." While not explicitly answered with a definitive "yes" or "no," the emphasis on automated optimization and understanding model-specific behaviors suggests that a universal standard might be less likely than the development of sophisticated tools and methodologies for prompt engineering that can adapt to diverse models.

Conclusion and Key Takeaways

The central argument is that achieving true "model agnostic prompting" is currently impractical due to significant performance degradation when prompts are transferred between different model families. The more viable and scalable approach involves developing automated processes for prompt optimization, underpinned by rigorous evaluation metrics. Understanding the unique characteristics and preferences of individual models, such as Claude's affinity for XML text, is crucial for effective prompt engineering. The future likely lies in intelligent systems that can adapt prompts rather than in a single, universally effective prompt.