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.
You run your eval suite. Agreement rate: 92%.
You run your new prompt three times. The outputs look good.
ProjectDiscovery was running an LLM-powered security scanning pipeline. 67.
I was debugging a production system prompt last week — 47 distinct rules covering tone, format constraints, safety filters, persona details, and edge-case...
A GitHub repository with 134K stars contains the extracted system prompts for GPT-5.4, Claude Opus 4.
I audited a client's production system prompt last month. 340 words long.
Most prompt engineers in 2026 still optimize the same way they did in 2023: change a word, re-run the eval, squint at the numbers, repeat.
Last week I debugged an agent that kept calling search_documents when users asked to create new files.
If you're still writing system prompts in a single text file and pasting them into an API call, you're operating the way we built websites in 1998 —...
You spent three days on that system prompt. Ran it through eval suites, tuned the wording, squeezed out every last percentage point.
Most prompt engineering advice assumes you've already picked a model.