Verified70% confidenceFactExact time
The effectiveness of prompt engineering is rooted in the large language model's In-Context Learning capability, which enables task switching without modifying model weights.
4
Sources
70%
Confidence
Long-term
Relevance
7/9/2026
First Seen
Sources
提示词工程入门：原理、方法与实战案例全解析
bilibili大模型零基础入门_6/30/2026
Related Claims
VerifiedIn-Context Learning (ICL) refers to the ability of large language models to perform new tasks without updating weights, solely by providing examples or instructions in the input prompt; this ability was first systematically described in the GPT-3 paper.82% similarUnverifiedLarge language models have largely alleviated the persona consistency problem through their massive parameters and in-context learning.79% similarUnverifiedWhen there are excellent writing examples in the context history, the model tends to follow the superior paradigm, which is related to the in-context learning capability of large language models.79% similarUnverifiedThe Skills paradigm uses the large language model itself to simultaneously perform intent understanding, slot filling, and parameter structuring, achieving zero-shot intent routing without requiring additional training data.79% similarUnverifiedUsing in-context learning technology, researchers can provide a description of dodo characteristics in the prompt as a reference to guide the model in making comparisons, without needing to fine-tune the model's weights.77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/403232API
curl https://kongchang.com/api/v1/knowledge/claims/403232MCP
get_claim(id=403232)