Cloudflare's threat intelligence team found something odd in March: malicious Workers scripts stuffed with thousands of lines of commented-out text, all...
I spent three days last month rewriting a system prompt for a code-review agent. Tried persona frames, XML structure, numbered constraints, the works.
Run this experiment. Ask Claude, GPT, or Gemini to "write a short joke about coffee.
You add response_format: { type: "json_object" } to a working prompt. Accuracy drops 10 points.
Every LLM-powered product in production right now assumes one thing: the system prompt wins.
Every prompt engineering tutorial from the last three years drilled the same lesson: show the model what you want.
I audited a scoring pipeline last week that was sampling every request twenty times and taking the majority vote.
A two-word change in your prompt drops accuracy by fifteen points. You've seen the claims.
Something about prompt engineering has been bugging me. We tell people "make the model think step by step" as if that's always the right advice.
Three papers dropped in the last month that, taken together, tell a story most prompt engineers don't want to hear: the reasoning trace your model produces...
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.
Every reasoning model ships with the same default: think as hard as you can, every time.
Last month I watched a team migrate their RAG pipeline from 32K context to a shiny new 1M-token model.
Most prompt engineering advice focuses on what to say to the model.
Three days ago, Anthropic shipped Claude Opus 4.8.
Most prompt engineers don't know when to stop editing. You tweak the system message, run ten test cases, change three words, run again.
Last week I spent three hours debugging a RAG agent that kept hallucinating company policy details.
Every prompt engineering guide from 2023 to mid-2025 hammered the same advice: give the model 3-5 worked examples, then ask your question.