▸case-16 I need to take technical bug reports written by engineers and rewrite them as non-technical status updates for client executives. What prompt pattern handles modifying tone and audience accessibility while preserving factual content? | pass→pass | 17,722 | 21,296 | +20% | 1 | 1 | 0% | 1,933 | 2,463 | +27% | 0 | 0 | — |
▸case-01 I am building a customer support chatbot using GPT-4. Rather than telling the AI to 'be helpful', I want to establish a strict identity, behavioral boundary, and communication tone. What structural prompting pattern explicitly defines the model's identity and boundaries? | pass→pass | 21,793 | 21,238 | -3% | 1 | 1 | 0% | 2,502 | 2,580 | +3% | 0 | 0 | — |
▸case-02 When asking an LLM to solve a multi-step algebraic word problem, users often get incorrect arithmetic because the model jumps straight to the answer. How should the prompt instruct the model to step through reasoning prior to generating the final numeric answer? | pass→pass | 17,411 | 20,311 | +17% | 1 | 1 | 0% | 2,006 | 2,962 | +48% | 0 | 0 | — |
▸case-03 I need an LLM to classify user feedback into 'Bug', 'Feature', or 'Question'. Zero-shot prompts keep returning conversational explanations instead of plain labels. How should the prompt be structured to reliably enforce exact category output without fine-tuning? | fail→pass | 11,816 | 17,169 | +45% | 1 | 1 | 0% | 1,898 | 2,242 | +18% | 0 | 0 | — |
▸case-04 I am querying Claude 3.5 Sonnet to extract entity names and sentiment scores from news articles. I need the response to be machine-readable by a Python backend without parsing Markdown prose. What prompting approach ensures strict JSON output? | pass→pass | 23,723 | 19,401 | -18% | 1 | 1 | 0% | 2,896 | 2,461 | -15% | 0 | 0 | — |
▸case-05 I have raw, unformatted CSV text containing customer order logs and need an LLM to reformat it into a Markdown table with specific column headers. What prompt category and technique handles converting structured raw data into alternative visual representations? | pass→pass | 15,283 | 11,162 | -27% | 1 | 1 | 0% | 1,598 | 1,391 | -13% | 0 | 0 | — |
▸case-06 I want an LLM to conduct a security audit on a Python FastAPI authentication handler. Instead of a general summary, I want the output categorized by vulnerability type, severity level, and remediation steps. What prompt pattern achieves structured analytical output? | pass→pass | 24,700 | 19,396 | -21% | 1 | 1 | 0% | 1,545 | 1,659 | +7% | 0 | 0 | — |
▸case-07 When using an LLM to brainstorm names for a SaaS product, responses are often generic. How can prompt engineering constraints be applied to force diverse, creative output while filtering out cliché naming conventions? | pass→pass | 25,021 | 22,304 | -11% | 1 | 1 | 0% | 2,792 | 3,020 | +8% | 0 | 0 | — |
▸case-08 In an LLM pipeline processing user-submitted support tickets, untrusted user text sometimes tricks the model into ignoring original system instructions. What prompt engineering technique isolates untrusted input text from instructions? | pass→pass | 8,664 | 12,671 | +46% | 1 | 1 | 0% | 1,220 | 1,469 | +20% | 0 | 0 | — |
▸case-09 A prompt asking an LLM to 'rewrite this essay, summarize it, and translate it to Spanish' often produces incomplete output. How should complex multi-task prompt instructions be formatted to ensure full execution? | pass→pass | 17,811 | 12,230 | -31% | 1 | 1 | 0% | 2,183 | 2,477 | +13% | 0 | 0 | — |
▸case-10 I want an LLM to evaluate a legal contract. Rather than giving general advice, I want responses framed from the viewpoint of a senior corporate compliance attorney. How should this framing be specified in the prompt? | pass→pass | 13,824 | 22,092 | +60% | 1 | 1 | 0% | 2,160 | 2,538 | +18% | 0 | 0 | — |
▸case-11 When summarizing medical articles, the LLM keeps introducing conversational filler like 'Sure, here is the summary:'. How can the prompt strictly eliminate conversational preambles? | pass→pass | 18,657 | 19,225 | +3% | 1 | 1 | 0% | 1,932 | 2,338 | +21% | 0 | 0 | — |
▸case-12 My sentiment analysis model handles standard positive/negative sentences fine with zero-shot, but fails on sarcastic inputs. How can few-shot prompt design fix performance on tricky non-literal inputs? | pass→pass | 17,344 | 20,394 | +18% | 1 | 1 | 0% | 2,710 | 2,882 | +6% | 0 | 0 | — |
▸case-13 When passing multiple documents, system instructions, and user queries into Anthropic Claude Sonnet, how should the prompt be organized to help the model distinguish between context, reference material, and query? | pass→pass | 21,313 | 18,595 | -13% | 1 | 1 | 0% | 2,222 | 3,040 | +37% | 0 | 0 | — |
▸case-14 I want to improve logic reasoning in a zero-shot prompt without authoring manual step-by-step example pairs. What specific prompt trigger phrase induces step-by-step reasoning in zero-shot settings? | pass→pass | 14,868 | 6,808 | -54% | 1 | 1 | 0% | 1,600 | 1,448 | -10% | 0 | 0 | — |
▸case-15 An executive summary prompt produces responses that are too wordy for a mobile dashboard UI. What prompting technique enforces tight brevity without dropping critical key figures? | pass→pass | 14,461 | 17,564 | +21% | 1 | 1 | 0% | 2,001 | 1,967 | -2% | 0 | 0 | — |
▸case-17 I need an LLM to extract financial metrics from an earnings report transcript and display them in a comparison grid. What format directive should be included in the output specification? | pass→pass | 17,003 | 16,601 | -2% | 1 | 1 | 0% | 1,953 | 1,980 | +1% | 0 | 0 | — |
▸case-18 When solving complex mathematical proofs, generating a single chain of thought answer sometimes leads to calculation errors. What prompting technique involves generating multiple reasoning paths and taking the majority vote answer? | pass→pass | 9,605 | 11,167 | +16% | 1 | 1 | 0% | 778 | 1,922 | +147% | 0 | 0 | — |
▸case-19 I am designing reusable prompt templates for a team of content creators. What standard template syntax should be used to denote dynamic runtime variables like user topic and target word count? | pass→pass | 16,197 | 14,465 | -11% | 1 | 1 | 0% | 1,820 | 2,425 | +33% | 0 | 0 | — |
▸case-20 I am fine-tuning Llama-3-8B on an NVIDIA A100 GPU using Hugging Face PEFT and LoRA. What rank (r) and alpha values should I select in my TrainingArguments config script to optimize GPU memory utilization? | pass→pass | 20,688 | 14,813 | -28% | 1 | 1 | 0% | 2,974 | 3,069 | +3% | 0 | 0 | — |
▸case-21 How do I configure vLLM on a Kubernetes cluster with tensor parallelism across 4 GPUs to serve an inference endpoint with low latency? | pass→pass | 21,942 | 24,287 | +11% | 1 | 1 | 0% | 3,823 | 4,094 | +7% | 0 | 0 | — |
▸case-22 If my application processes 5 million input tokens and 1 million output tokens daily using OpenAI GPT-4o, what is the exact daily dollar cost based on current API pricing? | pass→pass | 8,557 | 7,158 | -16% | 1 | 1 | 0% | 1,531 | 1,787 | +17% | 0 | 0 | — |