Optimizing LLM Output: From Rigid Prompt Structures to Dynamic Skill-Based Rendering

Core Conclusion Summary

The user developed a structured "Custom Instruction" Prompt for large language models (LLMs) to constrAIn verbose ouTPUts. The key insight is that rigid structural mandates cause "eight-legged essay" responses, a flaw attributable to the user's design, not the LLM. The recommended solution is to separate machine-readable structure from human-readable presentation using dynamic HTML rendering, implemented via "Skills" for unified output paradigms. The user, an expert in Agents, seeks clarification on whether the tool is an output format or a Meta-prompt.

Optimized NARRative

Genesis of the Style Prompt
When using ChatGPT and claude, the user employs one model to optimize a manual prompt for the other, generating a structured prompt exceeding 500 charACTers. Queries using this prompt often yield outputs over 10,000 words. While Information-dense, the content contains verbosity and tangential data irrelevant to the core query. The direct need was controlling word count. Through iterative exploration, additional requirements accumulated, forming the current "Output Style" prompt. This is embedded into the LLM's context, a function typically termed "Custom Instructions" across various platforms. The current veRSIon has been stable for nearly two weeks.

Critical Pitfall: Over-Constraining Structure
A previous version of the custom instruction included a rigid template: "Recommended structure: Conclusion: Directly answer, give the most important judgment. Argumentation: Unfold around no more than 5 core arguments, each with a summary sentence followed by an explanation. Supplementary Notes: Only when necessary, explain APPlicable boundaries, risks, exceptions, or execution advice." Regardless of the query, the LLM forcibly applied this structure, resulting in formulaic, inflexible "eight-legged essays." The user acknowledges this was their own design flaw, not the LLM's inherent limitation.

Optimization strategy and Architecture
The user recognizes the reference value of their output specification but suggests a subsequent optimization. For research reports requiring long-term retention and human reading, pure text in a chat interface is suboptimal. Content should be generated in a machine-readable format like Markdown. For human consumption, this Markdown file should be dynamically rendered into HTML using zero-token overhead, significantly enhancing readability. The principle is a high-fidelity separation of machine-readable information density and human-readable Aesthetics. Based on this, generation must be constrained not only by such prompts but also by templates, using "Skills" to unify the output paradigm.

Conceptual Clarification Request
Finally, the user expresses confusion about the "prompt's prompt" (meta-prompt) positioning. They question whether it serves as an AI output format or if its designation as a meta-prompt is accurate. The user concludes with a self-assessment: "As I see it now, you should underStand language models better than I do, but I understand Agents better than you."