Fifteen turns into a conversation, your system prompt might as well be a comment in dead code.
Last month I watched a team spend three days debugging a classification pipeline. The model had been 94% accurate in their eval suite.
Decagon runs a production classifier that reads customer conversations and outputs structured verdicts with reasoning chains.
You'd think a more detailed prompt would be a more effective prompt.
I set temperature=0.2 on a Claude Opus 4.
Datadog's 2026 State of AI Engineering report dropped a number that should make every prompt engineer uncomfortable: 69% of all input tokens in production...
You've been told to trust chain-of-thought. Make the model show its work, verify the reasoning, ship with confidence.
Sometime in early 2026, an attacker stuffed a crafted parameter into a Grafana log entry. Not a dashboard panel.
Ask a 70-billion-parameter model where Alice is standing relative to Charlie, given that Alice is north of Bob and Bob is west of Charlie.
Gartner declared prompt engineering dead in July 2025. Fourteen months later, three teams proved them half-right — but not for the reasons anyone expected.
You built a ten-agent system.
You've probably done this: a model gives a wrong answer, so you increase the thinking budget. More tokens, more reasoning, surely it'll get there.
The context window is 200K tokens. You crammed 180K of tool responses into it.
Three teams ran the same experiment — does formatting your LLM context as JSON, YAML, XML, or Markdown change the output?
Nobody looks at tool descriptions the way they look at system prompts.
Most of the prompts in production right now were written for models that needed help thinking.
The conventional wisdom has been dead for months, and most prompt engineers haven't noticed. An EMNLP25 paper from Cheng et al.
Sergey Brin told people to threaten their models. So Wharton's AI lab ran the experiment.
You read the reasoning trace. It looks perfect — the model identified the key constraint, considered two approaches, picked the right one, and explained why.