PromptHub

The Model Isn’t Wrong—You’re Just Bad at Prompting

Feb 22, 2025 · 8:54

Dan from PromptHub argues that prompt engineering remains critical for improving LLM outputs, covering Chain of Thought, few-shot, and meta prompting techniques. Chain of Thought breaks problems into sub-problems and is built into reasoning models; few-shot prompting works best with just one or two diverse examples, but can degrade performance on reasoning models like O1 and R1. Meta prompting uses LLMs to write or refine prompts, with PromptHub offering model-specific enhancers. For reasoning models, Dan advises minimal prompting, encouraging more reasoning instead of few-shot, and avoiding instructing the model on how to reason. Free resources include PromptHub's templates, the AutoReason prompt, and the Prompt Engineering Substack.

Prompt Engineering Tactics: Dan Cleary

Feb 5, 2025 · 5:12

Dan Cleary, co-founder of PromptHub, presents three research-backed prompt engineering tactics to improve LLM output consistency and reduce hallucinations. Multipersona prompting, from University of Illinois, calls on multiple AI agents to collaborate on complex tasks like writing a book. The "according to" method, from Johns Hopkins, grounds prompts to a specific source (e.g., "according to Wikipedia") and can reduce hallucinations by up to 20%. The motion prompt, from Microsoft, adds emotional stimuli at the end of prompts, improving output accuracy by 8% to 115% depending on the task. These tactics are available as templates on PromptHub. Cleary emphasizes that even small changes in prompts can have outsized effects, crucial for maintaining user trust in AI-integrated products.