prompt engineering means The craft of writing inputs to LLMs to reliably produce the output you want. It's mostly used in casual online conversation. It's casual and generally safe with friends and online. It's standard online vocabulary; nothing inherently concerning about hearing it.
prompt engineering
The craft of writing inputs to LLMs to reliably produce the output you want.
Etymology timeline
Born ~2021 · 1 year of recorded useSeen in the wild
Every time I read something about prompt engineering, I need people to understand what "magical thinking" is... If you think you are above it, you are definitely not!
Wait is this what they mean by “prompt engineering” This is delusional
can we get a conversation analyst on this. can we get a linguistic anthropologist doing a microsocial analysis of this genre of manly rationalist prompt engineering
Prompt engineering is the process of structuring natural language inputs (known as prompts) to produce specified outputs from a generative artificial intelligence (GenAI) model.
Effective prompt engineering involves understanding how a model interprets language, and may include techniques such as few-shot prompting, chain-of-thought prompting, and role assignment.
Definitions
The craft of writing inputs to LLMs to reliably produce the output you want. Includes role-setting, few-shot examples, chain-of-thought, and careful phrasing.
"Adding ‘think step by step' to the prompt doubled the accuracy — classic prompt engineering."
prompt engineering means The craft of writing inputs to LLMs to reliably produce the output you want. It's mostly used in casual online conversation. It's casual and generally safe with friends and online. It's standard online vocabulary; nothing inherently concerning about hearing it.
"prompt engineering" is slang. It means: The craft of writing inputs to LLMs to reliably produce the output you want. Register: casual. Use it in: casual conversation and online posts. Avoid in: formal writing and professional emails.
“Every time I read something about prompt engineering, I need people to understand what "magical thinking" is... If you think you are above it, you are definitely not!”
— Bluesky· May 2026“Wait is this what they mean by “prompt engineering” This is delusional”
— Bluesky· May 2026“can we get a conversation analyst on this. can we get a linguistic anthropologist doing a microsocial analysis of this genre of manly rationalist prompt engineering”
— Bluesky· May 2026“Prompt engineering is the process of structuring natural language inputs (known as prompts) to produce specified outputs from a generative artificial intelligence (GenAI) model.”
“Effective prompt engineering involves understanding how a model interprets language, and may include techniques such as few-shot prompting, chain-of-thought prompting, and role assignment.”
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Show 1 more dictionary-style sense (imported)
The technique of formulating instructions (prompts) for a generative AI, in order that it produce the expected output.
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Why is this section here?
Slang is read by different audiences for very different reasons. The For parents view re-explains the term in plain, non-judgemental language so a caregiver can understand what their child means without needing the surrounding subculture. The For English learners view focuses on register (when it's safe to use), formal alternatives, and common mistakes — what a textbook won't teach you.
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Semantically related
- tool use81%
The ability of an LLM to call external functions (search, code execution, databases) mid-conversation. The building block of agentic systems.
- few-shot78%
Giving an LLM a handful of input/output examples in the prompt before asking it to handle a new case. A cheap alternative to fine-tuning. "Few-shot examples in the prompt boosted accuracy by 15%." For complex tasks, three good examples often outperform a 200-word system prompt.
- temperature77%
An LLM setting that controls randomness. Temperature 0 = deterministic, always pick the most likely next token. Higher values = more creative / less predictable.
- chain of thought76%
Prompting or training technique where an LLM produces intermediate reasoning steps before the final answer, dramatically improving accuracy on multi-step problems.