Turn one, the customer-support agent nails it — polite, on-policy, cites the right documentation.
Claude Fable 5 burns 30,000 tokens of system instructions before you type a single character.
Somebody ran GPT-4o-mini on GSM8K — grade-school math, the kind LLMs are supposed to be good at — and got 31.8% accuracy.
Three years ago, a paper called EmotionPrompt showed that appending emotional stakes to prompts — phrases like "This is very important to my career!
Three teams ran the same experiments this year and landed on the same uncomfortable result: moving information around inside a prompt — without changing a...
Three days ago, Anthropic shipped Claude Opus 4.8.
Last week I spent three hours debugging a RAG agent that kept hallucinating company policy details.
You shipped the guardrails. You added the system prompt hardening, the input classifiers, the output filters.
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.