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.
You shipped the guardrails. You added the system prompt hardening, the input classifiers, the output filters.
On May 5, OpenAI swapped GPT-5.3 Instant for GPT-5.
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.
Your LLM got the math problem right 74% of the time. But if you'd asked it five times and taken the majority vote, that number jumps to 92%.
Last month a SaaS company posted their API bill: 42,000 per month on LLM calls, down to 2,100 after one infrastructure change. No model swap.
Last month I audited a startup's LLM spend. They were sending 100% of traffic to Claude Opus.
Datadog just published their State of AI Engineering report for 2026, and one number stopped me cold: 69% of all input tokens in production LLM calls are...
Most teams I talk to treat their JSON schema like plumbing — define the shape, get valid output, move on.