R0049/2026-03-31/Q001-SRC03-E01¶
Extract¶
The LLM-Prompt-Library repository contains categorized prompts including 5 medical prompts covering "clinical trials, diagnostics" and AI research prompts covering "benchmarking, experiment design" with a "statistical rigor" standards goal. However, inspection of the repository structure revealed no prompts implementing comprehensive research methodology frameworks (no GRADE, PRISMA, ICD 203, Cochrane, or IPCC implementations). The medical and AI research categories address domain-specific tasks rather than cross-cutting analytical rigor methodology.
Relevance to Hypotheses¶
| Hypothesis | Relationship | Strength |
|---|---|---|
| H1 | No support — library does not contain a comprehensive framework prompt | Weak |
| H2 | Weak support for absence claim, but limited to one library | Weak |
| H3 | Supports — presence of research categories without methodology content illustrates the gap | Moderate |
Context¶
This finding is significant as a negative result. The repository is one of the largest curated prompt libraries (covering multiple providers: OpenAI, Anthropic, DeepSeek, Meta, Mistral, Google, xAI) and explicitly includes research-adjacent categories, yet lacks research methodology prompts. This suggests the gap is not merely one of discoverability but of non-existence in the prompt engineering community's collective output.
Notes¶
The presence of a "statistical rigor" goal in the AI research category suggests awareness of the need for analytical rigor, but this has not translated into published methodology prompts.