Prompt engineering techniques, system prompt patterns, and LLM benchmarks — practical guides for developers who talk to machines for a living.
You spent three days on that system prompt. Ran it through eval suites, tuned the wording, squeezed out every last percentage point. Hit 87% accuracy on your test set. Shipped it. And then the support
Most prompt engineering advice assumes you've already picked a model. You tune the wording, adjust the temperature, add few-shot examples — all to coax better output from one fixed endpoint. But t
I spent three months in 2024 building retry logic for a pipeline that extracted product data from GPT-4. The model returned valid JSON about 94% of the time — sounds fine until you do the math on 50,0
Someone analyzed 3,007 Claude Code sessions and found a ratio that broke my brain: for every fresh token sent to the API, 525 tokens were served from cache. The total? 12.2 billion cached tokens again
It costs roughly one cent to jailbreak GPT-4o. Not with some hand-crafted prompt that took a red team weeks to develop — with an automated fuzzer that runs in about 60 seconds and succeeds 99% of the
I spent two days last month migrating a production extraction pipeline from GPT-4o to Claude. The prompts were clean. They'd been through three rounds of eval tuning. Every edge case was handled.
Turns out the thing that breaks your AI safety filter isn't some elaborate multi-turn social engineering attack. It's a newline character. Maybe a markdown header. Perhaps a humble list marker
You've been debugging your prompt for an hour. You've tried different phrasings, added examples, restructured the whole thing. The model still gives garbage. Here's a thought: maybe the pr
The single most repeated piece of prompt engineering advice from 2023 is now actively degrading your outputs. "Think step by step." Wei et al.'s 2022 chain-of-thought paper showed it cou