“How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning” by Entang Wang, Yiwei Wang, Aleksandra Bakalova, Michael Hahn.
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.
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.
"Few-shot examples in the prompt boosted accuracy by 15%."
““How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning” by Entang Wang, Yiwei Wang, Aleksandra Bakalova, Michael Hahn.”
Few-shot is an AI prompting technique giving the model a few examples before asking it to do the task. Tech jargon. Not concerning. Common in AI engineering. If your kid uses it constantly, ask them where they first heard it, not to police their speech but to understand which online community they are in. Most slang of this kind moves through TikTok and group chats, so seeing it in your child's vocabulary just means they are plugged into normal teen internet culture.
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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The vocabulary of software engineers, AI researchers, and anyone living in a terminal or on GitHub — from LLM to MCP, CORS to vibe coding, agentic to enshittification.
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It has a context window size of 2,048 tokens, and has demonstrated strong "zero-shot" and "few-shot" learning abilities on many tasks.
“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.”
“It has a context window size of 2,048 tokens, and has demonstrated strong "zero-shot" and "few-shot" learning abilities on many tasks.”
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A machine learning paradigm where a model is trained on a very small amount of data, typically less than that required for traditional machine learning approaches.
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