Ask GPT-5 "What causes rust on steel?" and you'll get an answer in under a second.
Last month I added chain-of-thought prompting to a medical Q&A pipeline. Hallucination rate dropped.
Everyone loves structured outputs. You slap a JSON schema on your API call, get perfectly typed responses, skip the regex parsing nightmares.
Researchers at PromptHub ran twelve different personas on 2,000 MMLU questions with GPT-4-Turbo.
You write a prompt. You test it.
Claude Opus 4.5 scores 45.
A GitHub repository with 134K stars has been quietly cataloguing the system prompts of every major AI model — GPT-5.4, Claude Opus 4.
Three years ago, few-shot prompting was the single highest-leverage trick in the prompt engineer's toolkit.
Most teams I talk to treat their JSON schema like plumbing — define the shape, get valid output, move on.
Meta just published a paper that should change how you think about giving LLMs hard tasks.
You run your eval suite. Agreement rate: 92%.
You run your new prompt three times. The outputs look good.
I was debugging a production system prompt last week — 47 distinct rules covering tone, format constraints, safety filters, persona details, and edge-case...
Midjourney spent a month being the fastest image generator nobody wanted to use. V8.
Last week I watched a coding agent lose its mind at the 35-minute mark.
Two months ago I ran the same benchmark prompt through GPT-5 three times. Same API key, same temperature, same max tokens.
A prompt template that gave gpt-4o a four-point accuracy boost on GSM8K turned around and cost gpt-5 over two points on the same benchmark.
Google rolled out a feature this week that, on the surface, looks like a productivity gimmick — save your Gemini prompts as reusable "Skills" and...
Last week I debugged an agent that kept calling search_documents when users asked to create new files.