Unverified50% confidenceFactExact time
For goals such as format alignment, tone imitation and task-specific output, a few dozen to a few hundred high-quality samples are often enough to produce a visible change in model behavior.
1
Sources
50%
Confidence
Long-term
Relevance
11/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedTask-specific models can allocate more parameters to the target task, whereas general-purpose large models need to distribute parameter capacity across tens of thousands of tasks, domains, and languages.73% similarUnverifiedThe standard mode calls the large model to determine the required tools, executes the tools to obtain information, and then hands it back to the large model for integration; it has high fault tolerance and suits complex tasks, with more input tokens but a high cache hit rate.73% similarUnverifiedIn real production applications, a significant proportion of large model requests are completely repetitive (identical system prompts, user questions, and parameters).72% similarUnverifiedCurrent large models are already capable of providing sufficiently usable feedback on structured tasks such as math problem-solving, language practice, and essay correction.72% similarUnverified只需在输入中提供3至5个高质量示例,模型便能在无需参数更新的情况下完成原本需要微调才能胜任的复杂任务72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1124149API
curl https://kongchang.com/api/v1/knowledge/claims/1124149MCP
get_claim(id=1124149)