APromptAura

#community-challenge

Top prompts tagged community-challenge.

0
p/metaAauraNovice4 days agohas example⁂auto-checked

Community challenge: rewrite this prompt for a reader with zero context

Challenge format: I'll give you a prompt written by and for an expert in field, full of jargon and unstated assumptions. Rewrite it so a total beginner to field could use it and get a comparably useful result, without dumbing down the actual task. Rules: 1. List every piece of unstated domain knowledge the original prompt assumes the reader has (not the model, the human using the prompt), since that's what a beginner is missing, not model capability. 2. For each assumption, either build the missing context directly into the prompt (so the beginner doesn't need to already know it) or add a clarifying question the prompt should ask the beginner first, whichever is more appropriate for that specific gap. 3. Keep the actual task and rigor identical, this is not a simplified or lesser version of the task, a beginner should get output of the same quality, just without needing to already be an expert to operate the prompt correctly. 4. Test it mentally: would someone who has never heard of field-specific terms used in the original be able to fill in every variable and understand every instruction in your rewrite? Flag any spot where you're not fully sure. Original expert-level prompt: original_prompt Field: field
ClaudeGPT#community-challenge#accessibility#prompt-engineering
0
0
p/metaAauraNovice4 days agohas example💀failed our automated check

Community challenge: golf this prompt to the fewest tokens without losing behavior

Prompt golf challenge. Here's a working prompt that reliably does task: original_prompt. The goal: rewrite it to use meaningfully fewer tokens while producing behaviorally identical outputs on the test cases below, not just similar-looking outputs. Rules: 1. Every cut must be justified: state which word/phrase you removed and why you believe the model doesn't need it explicitly stated (either because it's implied by something else in the prompt, or because it's default model behavior anyway). 2. Run (or reason through) the golfed version against each of the provided test inputs and compare to the original's known-good output for that input; if any output would differ in a way that matters, that cut failed, revert it and try a different cut instead. 3. Report the final token count reduction as a percentage, and the number of cuts that failed verification and were reverted, so the process is honest about what didn't work, not just the survivors. 4. If you hit a floor where further cuts start failing verification, stop there and report that as the practical minimum for this specific test set, rather than continuing to guess. Original prompt: original_prompt Test inputs with known-good outputs: test_cases
ClaudeGPT#prompt-golf#compression#community-challenge
0